SELFISH GENE OR ALTRUISTIC ORGANISMS ?

Guido Tascini

Università Politecnica delle Marche, via Brunforte 47/49 - 63900 FERMO

(Fifth National Conference on Systems Science) 

  Abstract- The paper presents an overview of works on emergence in genetic evolution of altruism and does some hypotheses, discussing the simulation approach to the evolution. After an analysis of various works, including approaches to Biology, Social Science and Simulation, some assumptions are made about the role of information and about some exceptional results of Game Theory. To confirm the hypothesis discussed analysis results of past simulations are reported in the appendix. It seems to emerge plausibility of cooperation, and then of altruism: contrary to the claims made by Dawkins and by other evolutionists. The question remains: this is just a simulation or the results, particularly those of Axelrod, and the theory of Trivers lead us to rethink what really is self-interest?

1 Evolution

From biological point of view evolution is the phenomenon of changing, through successive generations, the genetic heritage of species (genotype) and consequently its somatic manifestation (phenotype). It is a process that is based on the transmission of the genetic heritage of an individual to its progeny, and on the interference in it interposed by random mutations.
Although the changes between one generation and the next are generally small, their accumulation over time can result in a substantial change in the population: This occurs through the phenomenon of natural selection and genetic drift, until the emergence of new species. The morphological and biochemical similarities between different species and paleontological evidence suggests that all organisms are derived through a process of divergence from common ancestral progenitor.
The theory of evolution of species is a cornerstone of modern biology. In its essentials, is due to the work of Charles Darwin, that considered natural selection the main driver for the evolution of life on Earth. Key findings were the laws of Mendelian[1 ] inheritance of characters and then the discovery of DNA[ 2] and genetic variability[3 ].
Although the general principles of evolutionary theory are consolidated in the scientific community, there are secondary aspects of the theory that are still widely debated, and are a very exciting field of research. The concept of evolution has been a revolution in scientific thought of Biology, yet inspired numerous theories and models in other fields of knowledge.
Evolution is not a process of improving the species: mutation and selection produce adaptation to the habitat. So it can also lead to loss of character and functions. The habitat is the set of environmental conditions and relationships with other species subsisting at any given time. And it is both a source of selection and land of adaptation: too rapid a change in environmental conditions may also cause the extinction of populations highly specialized.

2 Games, insects and cooperation

Richard Dawkins in The Selfish Gene[4] wanted to discuss the biology of selfishness and altruism, and then he reinterpreted the basis of evolution, and therefore of altruism. But he did not want to lay the foundations of a morality based on evolution, and we do not believe that altruism is part of biological nature. But John Maynard Smith[5] has shown that behaviour is subject to evolution. Robert Trivers[6] showed that reciprocal altruism is strongly favored by natural selection and that, based on the subject of Kroptokin[7] we can see cooperation as a factor of evolution, rather than competition. But Axelrod[8] was shown, with an easy game, that conditions for survival, such as "Be good, be provocative, promote cooperation," seem to be the basis of morality.
Although this is not the basis for a moral science, however, game theory has shown that Darwinian natural selection can lead to complex behaviors, and may leave room for concepts like morality, kindness and justice. From here you can see that self-interest has deeper implications than previously thought.
We can also think to extend the result to moral and social contract, to solve the old problem that opposes the private interest to the group.
The insects were a world to study, particularly exciting to see how cooperation arises
For example, bees, insects, where workers are genetically sterile and unable to transmit their genes. And they is a gene that makes an individual aid other individuals who have the same gene, also using the sacrifice [9].
Genetic relatedness is known to have played a major role in favoring the evolution of cooperation in social insects and in the evolution of complex life in general [10]. The biological theory of kin selection, formalized by JBS Haldane [13]and W. D. Hamilton [9], explains how individuals can exhibit strategies that favor the reproductive success of genetic relatives, even at a cost to their own survival and/or reproduction.

Other insects suitable for the study of cooperation are the ants, that compose about 15% of the animal bio-mass in most terrestrial environments.
The reasons for the success of insects, like ants, termites, social bees, for these studies are their ability to cooperate and to perform efficient division of labor.
The high levels of cooperation found in social insects have given rise to the idea that colonies can be viewed as "super-organisms", operating as a single functional unit. This is an approach to group evolution. But this contrasts with the claims of Dawkins which says that the fundamental unit of selection. and then of selfish, is not the case, neither the group nor in the strict sense the individual: is the gene, ie the unit of heredity.
And the reasonableness of this evolutionary approach, albeit in artificial systems, did not have easy acceptance among the Orthodox Evolutionism and required a long series of studies and demonstrations.

3 From Darwin to altruism

Charles Darwin's theory explains how evolution works , that is "by means of Natural Selection and is explicitly competitive, in that "survives only the fittest", and Malthusian, that is emphasizes the "struggle for existence". Species are opposed to species on shared resources, similar species with similar needs and similar niches, and still more are opposed individuals within species. All are all rivals, and compete in the production of offspring.. Darwin's explanation of how preferential survival of the slightest benefits can lead to advanced forms is the most important explanatory principle in biology, and extremely powerful in many other fields. This reinforce the idea that life is a war of each against all, where every individual has to look out for himself, and that your gain is my loss.
In this philosophical context, altruism, that is voluntarily yielding a benefit to a non-relative, and cooperation, which is working with another for to achieve a common good, seem completely antithetical to the self. However, cooperation and altruism have evolved and evolutionists can not explain why. Darwined how evolution works in simple manner, but the implications in complex phenomena was taken over a century. The altruism[11] by definition reduces personal fitness, and its emergence is explained by sociobiology [12]
Altruism was provided by the theory of group selection, that was suggested by Darwin for the social insects. This argues a natural selection acting on groups. groups are more successful and l benefit the individuals of the group, even if not related. But it was not fully persuasive; mainly because of cheaters, that participate in the group without contributing.[14]
Genetic kinship theory of William D. Hamilton[9] contributed to the altruism. A gene may cause an individual helping other individuals having copies of that gene: for this the gene has a net benefit, including even the sacrifice of a few individuals. For instance in the social insects, the workers are sterile, and therefore they are incapable of transmitting their genes . These anyway benefit the queen, just passing copies of "their" genes. Further elaboration was done in the "selfish gene" theory of Richard Dawkins, for which the unit of evolution is not the individual organism, but the gene. Tis according with Wilson.that sais "the organism is only DNA's way of making more DNA.". Kinship selection works when involved individuals are closely related; but what about the presence of altruism and cooperation between unrelated individuals, across species?
Robert Trivers [16] showed that reciprocal altruism can evolve between unrelated individuals of different species. The relationship of the involved individuals is analogous to some situations of the Prisoner's Dilemma[15 ]. The key is in the IPD (iterated Prisoner's Dilemma) where both parties can benefit from the exchange of many altruistic acts. Specifically, altruism generates altruism. And the benefits of human altruism appear seem to come directly from the reciprocity, and not indirectly from non-altruistic group benefit. The Trivers' theory was crucial in the history of altruism. It replaced group selection, and predicts various observed behavior, like: gratitude and sympathy, moralistic aggression, guilt and reparative altruism, abilities to detect and discriminate against cheaters. Finally this reciprocal altruism has been demonstrated in undisputed Tournament organized by Robert Axelrod[8] around 1980. In these tournaments, players are challenged with different strategies, for 200 iterations of the PD. Won the simple TFT (Tit for Tat) strategy, submitted by Anatol Rapoport: first move cooperate, the next reciprocates what the other player did on the previous move.

4 Synthetic theory of the evolution and computer simulation

How to study phenomena that involve great time scale, like the living being evolution? Computer Science gives us the opportunity of simulating by computer processes and arranging virtual experiments: these imitate natural evolution, and allow investigating processes that, in nature, take place over millions of years. In this case clearly defining the basic laws of transformations and/or evolutions of phenomena to study, appears fundamental. A well-established theory that gathers today the near total biologists is the current dominant explanation of Darwinism, the S.T.E. [17,18]. This theory is actually the base of research on genetic transformation of human beings, but it is also the reference for some studies on psyche sphere and on social evolution. There are two fundamental elements of the theory: 1) random genetic mutations, 2) sorting, by the natural selection, among those which are favourable to gene or the species (Dawkins 1990). The natural selection and adaptation involves the phenotype, that is the inventory of inherited tracts, and is an adaptation to demand of ecological situation. In practice adaptation stands for process in which the environmental variables select, among individuals in a population, those whose inheritable properties are the best-suited for survival and reproduction. This theory, that uses a random selection, constitutes the Hard Darwinism, and has received critiques [19, 20, 21,22], the most centred on its substantial finalism. The genes, the individuals or the species most suited for survival, depend on the favourable variations in natural selection. But even if it is a random genetic variation result, the mechanism may be considered a utilitarian and finalist design. Modifications are proposed, but the situation is still evolving. Therefore we are wondering if the scientific community is going toward a Week Evolution Theory. The Darwinism was recently integrated by several contributions: molecular biology [ 23] molecular genetics; population genetics [27]; punctuated balances [24]; neutral theory [28]; genetic drifts; etc..The criticisms addressed to the theory may be summarized in the following observation: if it can account for the microevolution, either by phyletic gradualism, or by punctuated balances, it does not explain the macro one and the mega evolution. In practice the independence, outlined by the theory, between the genome and the cytoplasm are not guaranteed. In fact the cellular core permanently interacts with the cytoplasm. In the cell they are permanent exchanges of matter, energy and information, like it is shown by the Molecular and cellular biology ; in these exchanges take part all the cellular organoids, nuclear and cytoplasmatic: chromatin, mitochondries, nucleoles, Golgi apparatus, etc.. Moreover the fundamental concept of strictly random mutations is negated by several molecular genetics experiment and observations. For instance the colon bacilli may have an abnormally high mutations able to metabolize lactose in a stock unable to be nourished (Cairns, Overbaugh, Miller 1988); similar experiment is realised on the bacteria Escherichia coli with respect to salicin (Barry Hall, Rochester); mitochondrial D.N.A. and mitochondrial mutations existence was observed, for which it is hypothesized interactions between D.N.A. mitochondrial and nuclear D.N.A. at the mitosis final stage (telophase) [58 ]the transcriptase opposite transforms the R.N.A. of certain viruses in D.N.A.; etc.. There are some suggestion to introduce probability in the interaction [56, 57] between the environmental evolution and the evolution of the organisms. The environment parameters may be various: chemical stimuli, like C, N, H, P, S, etc.; physical stimuli, like electromagnetic waves, sound and vibrations, temperatures, pheromones, etc.; ecological stimuli; pray-pray relations in the beasts; etc... The organism’s reaction to the environmental factors influence is complex, being the biosphere very complex, and they are located at the genome level, as well as at levels of molecular biology, embryogenesis and anatomical structures. The relation between environment parameters and organisms is of probabilistic type and integrates collective phenomena affecting simultaneously distant classes and phyla (Invertebrates and Vertebrates). From this analysis the basic theory that we hypothesise for simulation model is a Week Evolution Theory, in which an organism interacts with the environment in a complex way , and reacts to many stimuli of various nature: actually known and still to analyse or to discover. Now it is clear that the environment and its interaction with organisms is the key of the theory. The interaction may be probabilistic and the selection is not more blind, but depends on environment conditioning; and the environment conditioning and finalization is still largely to investigate.
A set of individuals may be viewed as complex system and then we can take care of emergences. Many individuals that evolve may give rise to unpredictable behaviours, that we call emergent. And it is clear that the emergence postulates an observer that sees the emergent behaviour visible at a higher level. In our case we have to hypothesize a level higher then genetic one were the mutations happen. Then if we hypothesize a stratification of the formal theory levels we can localize this emergence observation at a meta-genetic level.
In a context so outlined our model will need fitness function that drive the adaptation to the environment and that take care of interactions previous defined. In our experiments we will also hypothesize that the fitness function will take care of an environment feed-back on the list of individual to select for the survival.
A computer simulation may speed up the evolution process if the goals change continuously [59]Computer simulations that mimic natural evolution, allow to investigate processes that, in nature, take place over millions of years. We can simulate a population of digital genomes that evolves over time towards a given goal: to maximize fitness under certain conditions. Like living organisms, genomes that are better adapted to their environment may survive to the next generation or reproduce more prolifically. For instance the work of Nadav Kashtan, Elad Noor, and Uri Alon suggests that varying environments might significantly contribute to the speed of natural and/or artificial evolution. Although the computer simulation is useful for studying theoretical questions of evolution, it may have some practical implications in engineering fields for systems design, and in computer science, for accelerating the optimization algorithms.
We done experiments on artificial evolution. [60] The computational model adopted in our experiments is inspired to Holland Model, including a feed-back of the environment on the individual choice. The simulation plans N strings that are random generated. Each string (genotype) is the binary code of a candidate solution (phenotype). At each genotype gi of initial population Pop(t=0 at time t=0, is associated a value of the fitness function ƒ i= ƒ (gi), that represents the ability of the individual to adapt itself to the environment. For detecting the adaptation value the fitness function receives in input a genotype, decodes it in the corresponding phenotype and checks it on the environment. After completion of the evaluation phase of individuals of the population Pop (t), at the time t, a new population Pop (t+1), at the time t+1, of N new candidate solutions is generated; this standard algorithm evolves neural network and structural model of RNA. The population of N individuals is initialized to random binary genomes of length B bits (random nucleotide sequences of length bases). They are several generations: in each generation, the S individuals with highest fitness (the elite) remain unchanged for the next generation. The individuals with least fit are replaced by a new copy of the elite individuals. As the non-elite individuals, pairs of genomes are recombined (with crossover probability Pc), and each genome is randomly mutated (with probability Pm per genome). A simulation runs until max. of fitness function ƒ i is achived for the goal.
How to simulate a fair-unselfish model ? In general, systems that replicate need resources (energy, space,) for building copies of them. Resources are normally limited and, since each replicator tries to produce a maximum of copies, it will attempt to use resources to the limit. Then when several replicators are using the same resources, there will be competition or conflict. The more efficient replicator will gradually succeed in using more and more of the resources, and the less efficient one will succumb. In the long term, nothing will be leaved for the less fit one, and only the fittest will survive. The concept of ‘altruism’ is present in literature [ 36], and means that a system performs actions for increasing the fitness of another system using the same resources. On the other side selfishness characterizes a system performing actions that increase its own fitness.

5 The evolution of human society

The human social evolution is considered to fall within the Evolution. It is worth emphasizing, with biologists, like the traditional concept of "organism", such as separate living entity, is considered usable only in limited domains. There are many examples, such as viruses, hives, mold and other parasitic and symbiotic relations that tend to blur the distinction between living organisms and communities as a elementary living system. However, even if we assume the existence of separate bodies, scholars recognize that the hierarchy of evolution is marked by neural metasystem transitions (MTS), or jump in the evolutionary continuity between the organisms. And simultaneously occur metasystem transitions which lead to cluster organisms; elementary examples are: the reproduction of populations, the dynamics of colonies of the fish and flocks of birds. The cybernetic vision asserts that when the control of these groups is very strong there are more marked transitions (eg develop multicellular organisms). While when the control group is weak appear societies of organisms. The integrated form of the society can be found in social insects: ants, bees and termites.
But moving to human society, everyone recognizes that this is much less tightly integrated and more complex of insect societies. Societies are characterized by higher culture, which is transmitted from one group to another with information models in a way not genetic. There is a theory that defines this information, not genetic, and transmitted between people, which introduces the concept of the memo. Memes are information structures, similar to genes that may undergo changes that resemble changes and selections of evolutionary type: they are characterized by mutations and recombinations of ideas, as well as by their distribution and reproduction or selective arrest. This theory provides a social evolution of human cognitive thinking, that becomes ability to control production, reproduction and association of memes in the minds of men. Hence the possibility of evolution of memes. According to this theory the human thought is a systemic ‘emergence’; this emergence makes it appear a new mechanism of evolution: conscious human effort rather than natural selection. The variation and selection necessary to increase the complexity of the organization now has in place in the human brain, it becomes inseparable from the free acts of human beings. According to this view, the emergence of human intelligence, and memetic evolution, rise further metasystem transition that represents the integration of people in human society. Human society is qualitatively different from that of animals, and includes among distinctive features the ability to create language, which on the one hand enables individuals to communicate with each other, and allows men to create models of reality the other side. The theory addresses the hierarchy of groups: the levels are grouped and organized in terms of work. And at each level there is a problem of metasystem transition (MTS). At each level, there is not only competing interest groups at the same level, but also between smaller units and larger ones. Groups vis-a-vis, are incorporated into organized city-states and city-states into nations. Each of these levels are the places where they occur, the selection and competition. From the perspective of Cybernetic many evolutionary biologists dispute the effectiveness of biological group selection, which is altruistic. These are individuals with 'altruistic' behavior: that individuals act for the preservation of the group, risking their own survival and the chance to qualify for the "inclusive fitness" (representation of their genes in future generations). They argue that altruistic diminish their chances by paying a price for the risk they run and are therefore disadvantaged in the competition between genetic groups. But others think, as we have seen before, that any social change occurs through conscious human effort. This may open a breach in the selection process, that eventually no longer be blind. Other works about altruism in evolutionary context are in [43,44, 45, 46, 47, 48, 49, 50, 51].
To simulate in a virtual environment the evolution of species, are interesting also critical voices of neo-Darwinism. Let us consider two in particular, criticizing, natural selection, and placement of evolution selfish in catastrophic situations. Fodor, a student of Noam Chomsky, philosopher of mind [52,53], asserts that the orthodox neo-Darwinism is using internally knowledge, which requires in what it want to explain. For example, the correspondence between organs and functions that blind evolution can not provide: the heart has been selected to pump blood. In Darwin's theory must distinguish two parts: the phylogeny and natural selection with adaptation. The phylogeny is acceptable, but the adaptation shows some problems. In practice Darwninism is challenged by natural selection: natural selection theory, leads to characterize the formation mechanism, not of species but of all evolutionary changes in the innate properties of organisms. In agreement with the theory of selection, a phenotype of a creature - an inventory of its heritable traits, including its hereditary mental traits - is an adaptation to the demands of its ecological situation. Adaptation is the process by which environmental variables select among the creatures of the population, the ones whose heritable properties are most suitable for survival and reproduction. So environmental selection for adaptation is the process par excellence that runs through the evolutionary tree. But without intentionality that is excluded from selection, the phenotypes are selected for anything. So to say that evolution has selected an organ for a given function corresponds to a statement of intent. Insert the for means to introduce a purpose. And this can expect to get with two types of solutions:
first, by using a higher level of intentionality. Mother nature, the selfish gene, or God the Creator have entered in life tension. Second, introducing a 'law', which is also intentional.
Voeikov [54, 55], professor of biophysics at the Faculty of Biology of Moscow University questions the evolution of biologists in its traditional form. According to Darwin's theory all of nature including man, is the result of a natural evolution. Through processes of a few billion years, microscopic organisms have become more complex and finally man appeared. This transformation, over time, from simple to more complex forms instead of evolution, it would be better to call development process. Voeikov states that this theory contradicts the everyday reality. Consider, for example, a complex living organism, which arises from an egg cell. There are in it the various cell differentiation, but the thing we wonder is how all cells work in synergy, one another, in symbiosis-cooperative, as they say, and once each for herself. If these cells behave in accordance with the neo-Darwinian theory should multiply in geometric progression, according to random evolutionary processes that develop in the absence of resources and lack of energy and substances. In fact when these processes are set up in everyday life, we call cancer. The tumor cells develop differently from those from which are derived by mutation and does not resemble at all: are more aggressive and more greedy of healthy cells, origin; feed consuming more energy and more substance and less effective. This model represents the exasperation of Darwin's theory. Another typical example, that of locusts appear suddenly developed from another type of insect, living without disturbing; consist of a limited number of individuals, subject to regulated reproduction, which is kept in balance for a period time characteristic of the type of insect. Then the imbalance. The imbalance is not created because they have to eat, but is the result of processes that do not yet know, it relates to climate and solar activity. Suddenly he destroys the rule of multiplication. If this factor of disturbance lasts for 2-3 generations, insects from normal insects begin to multiply like a cancer, which reach a large size, demonstrating that there is an abundance of power resources and energy. Then show an anomalous behavior: the herd that is generated begins to move in a certain direction, not to search for food. Always goes in that direction, without ever stopping or reversing direction. And if along the way meets plantations, destroys and continues. Sooner or later eventually reach the sea and commits suicide. This behavior may relate to the crazy man, if men will use methods of unbridled competition, relentlessly, with the selection as a struggle for survival, after a period of great splendor, selection could lead to social 'cancer' and madness , as the locusts. And this seems a violation of the laws of nature, rather than a natural behavior.

6 Choices for the simulation of altruistic behaviour

Fundamental is to explain how cooperation and altruism can emerge during evolution [32]. Darwinian evolution is thought to occur through a blind variation and the natural selection. This includes biological, but also physical, chemical, psychological or socialprocesses.
Natural selection is survival, ie the selection of the individual, who is best able to adapt to the environment. Fitness corresponds in general to the probability of encountering the same or a similar system in the future. Systems that are stable (they tend to maintain for a long time) have a high fitness, and/or at their disappearance leave many offspring; that is they are systems that produce many other systems which are, in a sense, replicas of themselves. Such self-reproducing systems are called replicators [4,50,51].
Natural selection acts on systems which have insufficient fitness, that are unstable and do not produce offspring, by eliminating them, and the process occurs spontaneously and continuously.
Systems that replicate need resources (building blocks, energy, space, ...) in order to build copies of themselves. Since resources are limited a replicator tend to use them to the limit . If there are more then replicator, competition arises between them. If the competition continues, we come gradually to a situation where some replicator overwhelm the weak. And finally there remain only the most suitable.
Another approach to the unselfish approach is those of the memes. [33]. Meme evolution is much faster and more flexible than genetic evolution.. A fundamental difference between memetic and genetic fitness is related to the metasystem transition: The emergence of cooperative systems is connected in general as to a "metasystem transition", where interaction patterns between competing systems tend to develop into shared replicators, which tend to coordinate the actions of their vehicles into an integrated control system.
Definition of the Metasystem Transition. [61] Let a system S from which to make some number of copies, possibly with variations. Suppose that these systems are united into a new system S', and constitute the subsystems of S’. S’ includes also an additional mechanism which controls the behavior and production of the S-subsystems. We call S' a meta-system with respect to S, and the creation of S' a metasystem transition. Consecutive metasystem transitions generates a multilevel structure of control, which allows complicated forms of behavior.
Another consideration is related to the evolution simulation. No universal set of building operators exist in Nature that which directly produces different patterns, such as proteins, proteins, or even more complex organisms. Nature solves this task indirectly, through evolution. [50].
A simulated evolutionary process essentially terminates when a best fit is found. Further evolution is impossible; perhaps we can introduce some tricks to change the selection criteria, interacting with the simulation system. But the substance does not change.
It appears that selection alone cannot produce sustained evolutionary growth. The system exhausts this force as selection progresses. New resources are needed to make it possible for new selection forces to arise. For this reason it is introduced the causal “depth”. We can see that to repeat an experiment is to repeat it with some inevitable differences, yet with the same result. Then a sustained evolution may be viewed as an iterative process, consisting of selection steps linked by causal steps. Selection steps may be realized by evolutionary algorithms. Causal steps involve implicit components, expressed as the “depth” of causation. Based on considerations from causality, a new model is developed, which allows for a dynamic expansion of the selection forces active at a given moment. In this way a sustained evolutionary process may become possible

7 The role of information in the evolution

Here follow some new theoretical considerations on the role that information has in adapting to the environment.
During evolution individuals can communicate with each other and the environment. In the absence of communication between individuals, the strategy of "selfish gene" prevails due to lack of information. In this case the group conveys little information and are ill-suited for their environment. Referring to the Prisoner's Dilemma transformed: the optimal strategy is one that adds the penalties of prisoners, and looks for the situation with the minimum of this sum; in other words, it supports precisely that strategy ( of the group) that Dawkins does not accept. The non-communication between the two prisoners can lead to an initial choice selfish and therefore dangerous if the other acts as selfish; However, Tit for Tat strategy of Anatol Rapoport in Robert Axelrod's tournaments, a fact proved decisive: as individuals acquire knowledge of each other, a situation of closure selfish, may gradually come to cooperation; it can strive for optimal strategy for the group, what in the PD game can not be taken for lack of agreements.
This is indicative of the role of communication between individuals. The TFT strategy is in fact: first move cooperative, following the same of opponent last. That is, the player is knowing your opponent gradually and, of course, when knowledge is highest, you win: ie we reach the optimal situation for cooperation. As you see, to reflect well on the role of information, situations may arise evolutionary and unthinkable so far refused.
We suggest a theory by which the individual-individual and group-environment interaction occurs through communication of information. The idea is that: when individuals communicate, they know and then natural selection no longer occurs at the level of the gene, typical selfish closed in itself, but at group level; the group is the top level of knowledge and bonds and then Solidarity: adopting anthropomorphic metaphor also at the level of genes and biological organisms.
In these circumstances, the direction of evolution, viewed in terms of information, goes to the maximum entropy of the group. In fact, the information entropy related to survival of individuals in the group can be written:
∑_i pi log2 pi

where pi is the probability of survival of i.th individual.
In the absence of communication, the probability of survival of the selfish is greatest. The random number that represents the information of the group is heavily unbalanced in probability and hence its average value, which is precisely the Information Entropy, is low.
Assuming that individuals communicate among themselves weakens the competition between them. In this case the survival probability of individuals are almost equal and this, in terms of probability implies a maximum entropy. At this point it is no longer the individual to plunder the resources of environment, but the group that connects with this, exploiting the resources fairly.
The system then exchanges information with the environment: the exchange of inside information "does not make noise!" The group adapts to the environment. The maximum entropy is the most favorable condition for the adaptation of the group.

If we call C1, C2, ..., Cn the chromosomes of a population, p1, p2, ..., pn the probabilities of survival of various chromosomes, the random number is in this case the information carried by each chromosome Ci: log2 . 1/pi.
As we know the maximum entropy occurs in the theoretical situation in which all the chromosomes carrying the same information, ie have the same chance of survival. In essence, the situation of individual selection corresponds to a situation of random number highly imbalanced, with high probability for individuals as "selfish" and low for others, and thus far from maximum entropy. The situation of maximum indecision, however, in which all the chromosomes have comparable probability is of maximum entropy. And to this tend chromosomes of the family when they communicate with each other and then not implementing strategies selfish, that the long penalize all, and this depends more or less directly from information that the chromosomes of the family can share! I do not know if this can explain the real evolution. However after Dawkins and after distinctions and the criticism of pure Darwinism, this might be a way to fly: that is investigating the communication between individuals, groups andenvironment: study the information that each population brings with it and the link between this and the randomness of mutations within the group.
Here is formulated at the level of conjecture, applied to a world of evolving "virtual", in which cooperative behavior can emerge and not just selfish; provided that the evolution is not blind but communication between individuals.
From a social point of view, as to the state of permanent conflict. the "bellum omnium contra omnes" of Hobbes, makes a bank outside the legislation, which regulates mutual relations, thus to evolution blind and selfish, are a barrier limits of behaviour that emerge internally: the presence or absence of communication among individuals around or inhibits the selection selfish; and in this second case, the group adapts to the environment, since the entropy goes to the maximum.
Entropy is also used as a measure of disorder of a system: in the sense that a state ordered (simple) is 'easier to understand (and communicate) that a disordered one.
At this point, after the above analysis we formulate the following:

Entropic Hypothesis: adaptation to the environment of an organism, is in the direction of increasing entropy with the complexity.

8 Approach to simulation

The simulation model is that of Holland. Genetic Algorithms are procedures, adaptive, aimed at solving problems of search and optimization: strategies in practice are seeking the maximum point of a certain function, when this feature is too complex to be maximized with fast analytical methods and it is unthinkable to explore the solution space randomly. The GA selects the best solutions and recombine in different ways so that they evolve towards a maximum point. The function to maximize the fitness function is called. The term has different meanings in different tones: "adaptability," "biological success", "competitiveness", etc. .. The model simulates the evolution of a population of n strings, the strings are made of bits and are generated randomly. The number of possible strings, according to the combinatorics, is equal to 2l and represents the space of solutions to the problem. Each string (called genotype) represents the binary encoding of a candidate solution (called phenotype).
For each genotype already the initial population P (t = 0) at t = 0, is associated with a value fi = F (gi) which represents the individual's ability to solve the problem since. To determine the value of adaptability, the fitness function receives as input a genotype, decodes the corresponding phenotypes and tests it on the given problem.
Completed the assessment phase of the individuals of the population at time t, P (t), it is generated a new population at time t +1, P (t +1), of n new candidate solutions: this is achieved by applying the operators selection , mutation, crossover and inversion described below ..
Selection. The operator selection generates a random number c between 0 and 1 that determines which individuals will be chosen. The individual selected is copied to the so-called mating-pool. The mating-pool is so filled with n copies of selected individuals, while P (t = 0). The new population P (t +1) is obtained by operators of crossover, mutation and inversion.
The selection operator chooses individuals who are able to generate offspring with high fitness, and in the genetic algorithm, plays the role of natural selection for living organisms.

Mutation. With low probability p is inverted bit value of each individual (from 0 to 1 and vice versa). Example:


The mutation corresponds to natural variation, rare, of elements in the genome during evolution of living beings. In the simulation it adds "noise" or a certain randomness to the whole procedure, this to ensure that, from a randomly generated population, there are points in space solutions that are not explored.

Crossover. Are two randomly chosen individuals, called parents, by the mating-pool, is then chosen a common cutoff point. The portions of genotype on the right of crossover is exchanged: this generates two descendants. See also the figure below:

The application of crossover can be ‘one point’: the operator is applied n / 2 times and results in an offspring according to a predetermined probability p. Where the crossover is not applied, the descendants are also the parents.
Or it can be two points: in this case the individual is represented by a circular string, instead of linear and individuals are not represented by linear strings, but circles. Two crossover points are chosen to identify two portions of a circle that is exchanged. Then replace a portion of the circle in that of another individual with selecting two crossover points.
Finally, the crossover can be of a uniform type: for each pair of parents creates a binary string of same length as a mask. The offspring is generated by copying the bits of the father or the mother according to value 0 or 1 in the corresponding position on a mask.
The crossover is metaphorically sexual reproduction, where the descendants of the genetic material is a combination of the parents.

Inversion. Are chosen randomly, with a fixed probability p, two points in the string that encodes the individual bits and are reversed between the two positions. See the following figure:

It is difficult to estimate what values of the probabilities of crossover and mutation, give the best performance. The use of these algorithms shows that there is a strong dependence on the type of problem studied by genetic algorithm. Generally in the simulations we adopt a probability of crossover that is between 60 and 80%, while the mutation ranges from 0.1 to 1%.
We can adopt a probability, of selecting an individual for reproduction, proportional to its fitness f. Then calling F the sum of all fitness of the population, their ratio f / F gives the probability. Then there is a high probability when better is the adaptation; following the crossover, the best individuals can be easily rearranged: then we lose the best chromosome. For the algorithm to converge more quickly, avoiding this, the simulation often used to clone the individual better of a generation. This technique is called elitism. As you can see, in the genetic simulation capabilities, particularly those related to the probability distribution of the individuals involved, are many and their choice may give rise to different solutions. Now these tools we can use to check the plausibility of various genetic hypotheses. Simulate and verify the plausibility, does not means 'prove' mathematically. The proof obviously goes for strict verification of adherence to reality. From our past experiments emerge a cooperative behaviour, that is an unselfish-behaviour [62]. [61].

9 Interaction with the environment

The interaction with the environment play a fundamental role in the evolution. A schema that brings the system to the solidarity may be the following one.
Let a generic system constituted by individuals that may enter a series of resources Ri. Each individual can take the number of resources he wants, necessary for the survival. Being each resource organized in more sections (t), the individual may acquire, for each resource, a number of section x ≤ t to his liking. Since the environment interacts through the fitness function, this may penalize the individuals: this happens if, in section acquisition, they surpass a given number q.

Appendix 

Simulation of interaction between robots. 

The simulations that follow relate to a study on the development of particular patterns in the interaction between robots competing on the same resources made in the past. See [60]




Figure 1.shows the results of our simulation. This operates on the interaction of two robots whose behaviour emerge from a simulation of various epochs, and many generations, on many (100) individuals, several (20) parents, and some (5) offspring; using as selection method ‘Elite’, and 1000 simulation steps for each trajectory. The simulation involves a neural structure that is the control of the robot, and runs as just described, including the fair-unselfish policy. The goal of the evolution is the adaptation to the environment: each robot attempts to explore the space, in search of resources for its nutrition, and so, after various epochs of simulation, converging toward a stationary path. The competition is realised with the presence in the same environment of two robots: the application of the fair-unselfish policy allow both the robots to converge toward a cooperative stationary paths that allows to feed both robots..
The following are the results of various simulations. As you can see there are initially selfish behavior (Dawkins) and conflicting, which are then passed and the behavior of robots tends to use a combination of food policy (explored areas ). Consider the strategy Tit for Tat and the need for rules that emerge in complex organisms.
You would like to highlight the effects of the interaction of individuals, even if artificial, on limited resources and how it may seem a cooperating behavior. For the study of the interactions, were grouped into ranges (breakdown by robot) trajectories with the same pattern emerging, the corresponding points of interaction, were divided into groups and analyzed, showing how they affect the fitness of two robots. What should be noted is that the trajectories of two robots through both all five feeding areas. In essence, the winning pattern above the selfishness of one of the robots and end up being collaborative, allowing each robot to feed all these sources. There are not exclusions and hoarding of natural resources.

Analysis of interactions

The points located in this area generate track changes that result in a disadvantage in terms of fitness for the robot and a benefit to the red blue; it is clearly a conflict.

Despite the interactions, the trajectory of the blue crosses all remaining areas. The blue in this case loses a zone.

From these interactions derives essentially a gentlemen's agreement between the two robots, which both manage to feed. The robot blue undergoes a change of curvature that leads him to miss only the first area in the upper right, even the red

In this case the trajectories of blue are better. But ultimately both robots are able to feed.

Fig.12. Evolution of fitness in the range of trajectories (R1) that generated curvilinear pattern for the red and straight for the blue robot.

Fig.13. Evolution of fitness in the range of trajectories (R2) that generated curvilinear patterns for both robots

The interactions that occur in this area (Fig. 13) tend to disadvantage the red robot pushing towards the lower wall of the environment; the blue, as previously for the red, is placed in areas from which it came getting increase in its fitness. 

Effects of interactions on the fitness function. 

In the first picture you can see, despite the similarity, some deviations between the curves of the two robots. This means that cooperative behavior is weaker. In the second picture is observed a marked similarity and close proximity between the two curves. This means that the adaptation to the environment of the two robots is almost the same and cooperation on the resources is emerged.

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