The paper suggests that natural evolution also involves the discovery and assimilation of hidden laws and correlations of the environment into the genetic makeup of organisms. Based on a simulation study of evolution in an artificial world with its own hidden laws, we suggest that the genetic algorithms of nature explore manifestations of hidden laws, like causal connections and correlations between events in the physical world. A chain of such causal connections and correlations (which we call a domino event chain) can be assimilated into the genetic makeup of an organism over evolutionary time, which would show up as its instinct. We also present an abstract and simplified model of evolution based on creature logic and environment logic in an attempt to explain how such discoveries might take place. This model is then used to suggest how insect colonies and other interesting relationships among species might evolve.

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Rekesh John,Technology Director.
California Software Co. Ltd.
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C.R.Muthukrishnan
Dept. of Computer Science and Engineering
Indian Institute of Technology
Madras, India
crm@iitm.ernet.in

February 1997

Index

• Introduction
• An artificial world
• Simulation
• Simulation Results
  • Behavior vs. Internal efficiency

• Discussion
  • Evolution in a logic network
  • Colonies and distributed control
  • Behavioral attractors

• Morphogenesis
• Conclusions
• References

Complex and intricate patterns of interaction are all too commonly observed in nature. A bee gets attracted to a flower, lands and drinks nectar and flies off to the next one. It has started an event sequence of pollination which leads to further events in the lives of the plants and the bee as well. A newly born turtle on the beach moves in the direction of a brighter light gradient and enters into the ocean where further events await. It does not "know" that the ocean exists and it is supposed to go there. It was conveniently born at the sea shore, ready to go in when it emerged. A parasitic worm that infects cows spends its larvae stage in the abdomen of an ant. One of the larvae burrows its way into the brain of the ant, changing its behavior so that during cold evenings the ant climbs up a blade of grass and hangs on to it with its jaws. The cows that graze in the evenings devour these ants and the larvae make their way into the cow's abdomen where the rest of their life histories are played out.

That there exists an intricate web of interaction logic in nature is undeniable. Almost any trivial event like throwing a stone into a lake can have far reaching consequences which may not be easily traceable. These consequences may vary from instance to instance of the causative event. However, there will usually be a subset of such events (consequences) that are "invariant" so to speak - meaning that they occur "as a rule". Formation of ripples is an obvious consequence when throwing a stone into a lake, and so also the contribution of the ripples to the minute sand beaches at the edge of the lake. These "invariant" events may be considered to be manifestations of the "laws" of the environment.

Can organisms over evolutionary time genetically "discover" and thus make use of hidden laws of its changing environment, laws which are not apparent during its own lifetime? That is, if creatures are exposed to varying environments during their evolutionary history, won't the genetic drive result in their "discovering" to advantage what is common among all the variations they have experienced? Won't they use these discovered laws, principles, correlations or whatever we may call them, constructively? For example, the sky has been relatively the same for millions of years. Don't this mean that many species shall learn to navigate (among others) by the sun and the stars, given the known history of the earth?

We use the term domino event to refer to a causal connectivity or a correlation between two sets of physical events. If physical event e₁ causes event e₂, or there is a correlation between event e₁ and e₂ in time, then e₁ -> e₂ is called a domino event E. A domino event chain would be a sequence of domino events E₁ E₂ E₃ ... E_n , such that e₁ -> e₂ -> e₃ -> ... -> e_n+1 where E_i represents the e_i -> e_i+1 connection. Note that a domino event is not a physical event in itself, but a logical connection between two sets of physical events, that one set implies the other under some circumstances. Any physical event e may internally be composed of sub-events in combinations. A domino event is an information abstraction, expressible in terms of logic, similar to a rule in a production system, or to some circuit in a logic network. Domino event chains may be viewed as representative of the laws and correlations of the environment. It is suggested that evolution also involves the discovery and assimilation of domino event chains into the genetic makeup of organisms, which show up as their instinct. To make a crude analogy, evolution might discover correlation chains within the logic of the environment, like an inference engine that runs through a production system and discovers new inferences. The sections that follow attempt to elucidate on this concept.

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Here we describe a study of evolution in an artificial world of our own creation, a world with its own hidden "laws" and correlations.

The world consists of an MxN cylindrical grid in which organisms are populated. An organism at the beginning of its life finds itself somewhere on the first row (of the M rows) of this world. Reminiscent of the snakes and ladders game, this world is populated with three types of snakes and ladders (Fig. 1). The "ultimate goal" set for an organism is to reach the highest row, viz. the Mth row of the world, navigating the snakes and ladders on its path.. An organism encountering a snake's head is swallowed, to find itself at the tail end of the snake. Similarly, an organism encountering the base of a ladder would find itself at its top. The snakes and ladders are "colored" red, green or blue.

A world is generated according to certain rules. The maximum and minimum possible number of each type of snake and each type of ladder in the world is fixed. They may be positioned almost arbitrarily in the world, but subject to the following rules:

1. Whenever a red snake is generated into the world at some arbitrary position, there will always be a green ladder starting two cells away to the right from the tail end of the snake, on the same row. This ladder will be longer (in rows) than the red snake it accompanies.
2. Whenever a red ladder is generated (again at some arbitrary position), there will always be a green snake starting at the cell just above the top of the ladder. This snake will be longer than the ladder it accompanies.
3. Green ladders and green snakes may also occur freely, independent of any correlations with the red snakes and ladders.
4. Blue snakes and blue ladders may occur freely, without any dependencies at all.

Organisms are embodied with sensors, actuators, memory and some DNA. This DNA is but a string of bytes, interpreted as a set of operational instructions which may be used for evaluating the sensors, moving a step in some relative direction, or performing some arithmetic and logical operations. An organism can potentially sense only its immediate neighborhood of atmost 8 cells. Its behavior is controlled by a DNA transcription logic, which is reminiscent of those in prokaryotes [4]. A gene is defined as having a header, weight, codons and a trailer (or the header of another gene). The header (H) and trailer (T) are bit patterns which tend to attach or detach an "executionase" (E) (Fig. 2). The "executionase" is an abstract entity that has affinity to a particular bit pattern on the DNA sequence. The executionase "attaches" to a gene and moves along it, interpreting the "codons" until it finds a trailer pattern or gets prematurely disassociated by some specific codons (instructions). Some codons may cause the executionase to "jump" across a specified number of codons within the gene as well.

The affinity of the "executionase" to a gene depends on the "closeness" of the gene header pattern to a specific pattern of the executionase. Therefore it is this affinity that defines the number of active genes in the DNA sequence as well as their order of execution.

The gene is also endowed with a dynamic weight (W) which is not used in the simulation described here. This weight can be modified during the lifetime of the organism, changing the affinity of the executionase to the gene dynamically. Such a mechanism is useful in allowing an organism to learn from the environment (changing the weights based on positive and negative feedback). However, a modified weight is not transmitted to progeny, thus avoiding Lamarckian inheritance.

The sensors and actuators are memory mapped to make their control easier. A memory read of a sensory location "evaluates" the sensor and returns an integer value. A memory write to an actuator may cause the organism to move in some relative direction, or emit pheromone (of a type depending on the actuator). In this simulation, however, pheromones are not used.

Thus the memory, the dna string, and the executionase together represent the organism. The executionase "runs" continuously, interpreting the codons and thus generating the behavior of the organism.

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A population of P organisms are generated from random dna sequences. For each such organism, a world is generated subject to the rules mentioned earlier and the organism is placed at a random column on the first row of the world. The executionase of the organism is activated and is allowed to run for a maximum of CYCLEMAX cycles or until the organism reaches its goal (the Mth row) whichever occurs first. The fitness f of the organism in this world is then computed. This process is repeated for Nw newly generated worlds for the same organism, and the resulting Nw fitness values are averaged to get the effective fitness e of the organism (see Fig. 3). This is done to counter the possibility that an organism may get lucky by chance meeting of some long ladder. Therefore the true worth of an organism is estimated by putting it through a number of worlds or "situations". In nature this is so, since almost every day organisms replay their strategies of survival.

The above procedure is repeated for each of the P organisms. The top 10% best fit among these are selected and random mutation and cross-over are applied to generate a new population of P organisms. The simulation is iterated over this new population.

The fitness criteria is based on the number of moves (m) the organism makes in the world, as well as the row ( r ) that it finds itself at the end of a run. The term r² is used so that an organism that has reached the 6th row at the end of a run by making 3 moves is more fit than that which has reached only the 4th row making 2 moves.

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We used a grid of size 100x20 (to reasonably fit on a graphics display), a DNA string size of 2Kb, mutation rate of 0.01% with Nw=(10 to 20) and an initial population P = (300 to 1000) organisms in various runs. The codons represented 2 or 3 byte instructions for memory, arithmetic and logical operations. Simulations were run for weeks due to CPU requirements. The following behavior were noted (by picking samples of the 10% best-fit in the populations and "executing" them graphically).

1. The organisms learned (over evolutionary time) to recognize snakes as dangerous and ladders as beneficial.
2. Many of them learned to avoid the red ladder initially, but later overcame the difficulty.

3. They learned to recognize a red snake as beneficial, and would take the plunge, make the two steps and climb up the green ladder.

   Sometimes this behavior evolved within a few hundred generations, and sometimes it took a few thousand generations.

4. The best performers often had a tendency to move diagonally in the world, probably because this would result in their encountering more variety. An organism moving straight would get to sense only 3 new cells during each move in an 8 cell neighborhood. But a diagonally moving one would get to sense 5 new cells per move. With a genome fit to handle these more frequent encounters, they moved to their "goal" faster than those which moved otherwise.

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Initially the fitness criteria used the number of cycles of CPU time an organism took rather than the number of moves it made. Thus the best performer was decided based on the amount of CPU time taken. The result was disappointing. Neither behaviors (2 & 3) emerged, and the populations tended to become uninteresting and monotonic. That is to say, it looked like behavioral variety soon ceased to exist. Because of the evolutionary drive for tighter and faster coding the populations were restricted in developing complex behaviors. Then we realized that, in nature, the environment rewards behavior, and not the internal efficiency of organisms. Thus it meant that if we wanted complexity of behavior to increase, then the fitness criteria should evaluate the organism's behavior as it is apparent in the world. Therefore the criteria was changed to the number of moves an organism made in the world. This however is not meant to imply that internal efficiency is not a criteria, for it would be reflected n behavioral efficiency. In our case, if an organism took too many CPU cycles to make a move, then it would make only a few moves and end up consuming CYCLEMAX cycles. Its would tend to be less fit than one that moved faster and consumed less CPU cycles. One that moved too fast and consumed very small amount of CPU cycles would tend to be "dumb" and thus less fit.

Fig 4. is a plot of the fitness of the best individual in a population, over generations. The y axis is the approximate number of moves an individual took to reach its goal. Actually it plots the value (100²/e_f). It can be seen that the fitness fluctuates within a band, owing to variations in the environment, where one strategy may not always be as successful as it was in the previous round.

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In the simulation described above, the organisms discovered "hidden" rules or correlations within the environment to their advantage. These rules or correlations remained invariant, so to speak, over the generations, and were assimilated into the genetic makeup of the evolving organisms. One may extend the rules so that correlations may exist between say, the position of green ladders (associated with red snakes) and blue ladders, which the organisms will make use of in further evolution. One might say that evolution tends to internalize chains of correlations (what we call domino event chains) into the genetic makeup of its organisms. These correlations need not be discovered in any particular order unless constrained by their nature. Parts of a complex correlation web which is persistent over evolutionary time may thus be internalized in terms of small correlation chains, leading to leaps in species fitness.

To pick an example from nature, female canaries are brought into reproductive condition by a change in day length and other external stimuli. These result in a change in the endocrinal state, leading to nest building behavior. Stimuli from the resulting nest cause further changes in the bird's behavior [3]. To cite examples of inventions in biosystems, we may consider the cilia and flagella "motors" of the bacteria E. Coli, the transport mechanisms in cells, photosynthesis in plants, the sonar of bats, electric eels, the chemical gun of bombardier beetles, migration of birds using the earth's magnetic field and so on. One might be tempted to predict that nature is abounding with plenty of such hi-tech biosystems, especially among species which have a low life-time expectancy and high reproduction rate.

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We would like to suggest a simplified scenario as in Fig. 5 where the environment is depicted as some complex logic network with state. This environmental logic network has input/output terminals which may be tapped by organisms using their sensors and actuators. The inputs to this net represent actions performed by the organisms on the environment. The outputs are sensory evaluations of the organisms or actions performed from the environment on the organisms. These organisms are also pictured as logic networks, their organization dictated by dna. In this scenario, creature evolution may be viewed as the functional optimization of the creature networks based on interactions with the net terminals. The genetic algorithms of nature would then cause the creature logic, with its limited set of input/output terminals, to "guess" the functionality and features of the net to its benefit. Mutations would help not only in modifying creature logic, but also in tapping new terminals. Thus over evolutionary time, creatures should be born with some "knowledge" of certain causal connections and correlations of its environment. A knowledge which we would call instinct. This "knowledge" would consist of a set of wired-in responses to certain cues or signals from the environment logic. In nature, some species of fish (as well as some trees and plants) emit certain chemicals into the environment when attacked. Other fish (or trees or any threatened species) could genetically discover this correlation and act upon it. No doubt such correlations exist plentiful in nature, and may lead to interesting hunting and evasion skills among species. In the snake-and-ladder world, the discovered behavior 3 (under simulation results) could be extended by more correlations so that the organisms would follow up behavior 3 with yet another. Thus automated responses may also involve a sequence of actions towards accomplishing a beneficial task.

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In Fig. 5, creature logic interacts with one another through the environment logic. The environment logic would be explored by variations of signals on the input/output terminals, or by selection of new terminals. To quote an example, there has been instances of electrical transformers attracting and killing mosquitoes in large numbers because of the electrical system emitting certain frequencies that resulted in a mosquito response. Evolution should drive creature logic systems to discover new ways of interaction by making use of hidden subsystems within the environmental logic. Perhaps insect colonies would be of interest in this perspective. A colony may be thought of as a distributed logic network consisting of individual creature logic networks, controlling and/or being controlled by one another through the environment logic. Consider a termite colony for example. The number of termite soldiers in the colony is regulated automatically without any centralized control. The soldiers emit certain "pheromones" which suppress the morphogenesis of eggs into further soldiers. When some soldiers die, their pheromone concentration comes down and some eggs start developing into soldiers, to replace the ones that died. In such colonies we usually find distributed and centralized controls which result in emergent behaviors when actions are performed en masse. (e.g. bees fanning their wings to keep the hive temperature under control).

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There are other interesting possibilities to be considered. Given that correlation/causality chains may get assimilated and complex interactions might develop, these chains may form attractors. Random boolean networks are known to settle into attractors quickly [4]. This may not be the exact situation here, but we might expect some assimilated set of actions of an organism to be stable and repetitive due to certain recurring signals from the environment. Like a gramophone needle getting stuck, playing a short piece of music again and again. Such a set of recurring actions may be called a behavioral set. If the causative recurring signal ceases to exist, or a higher priority one comes into existence, then the creature logic system might fall into some other attractor, resulting in the organism exhibiting some other behavioral set. We may be thus tempted to think that apparent creature behaviors, as diverse as foraging, fighting, courting, breeding, parenting, etc. may have some connection to the formation of such attractors. Remove a mother bird's nest of eggs and it may initially fret about for a while and then go on its way (perhaps foraging) as if nothing has happened. A young bird in the nest opening its mouth or pecking at its mother's beak and the mother bird regurgitating food into its mouth could be a set of event sequences based on mutual triggers. Cases of neglect by parent birds of its young which have fallen just outside the nest rim also imply their following event sequences based on triggers [3].

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All these interactions ultimately have a cellular basis. An organism is able to interact highly meaningfully with another of the same species due to genetic programming that defines its instinct. We may call this as self-referentiality, where a genome is programmed to interact with copies of itself. The same would be true at the level of cellular interaction. Cells are able to coordinate with one another and form a "society" with rules and controls and some wired-in knowledge of the complete system i.e. the body of the organism. Specializations within an insect colony, like the queen, workers, soldiers etc. may be compared, though at a different scale, to cell specialization. Each soldier in an insect colony responds to specific signals from its environment and is "trapped" in one or more behavioral attractors. The same is the case among cells. It is quite conceivable that the complex cellular interactions during morphogenesis result in patterns of cellular signalling (attractors) that cause the cells to specialize in many ways.

Earlier we noted an example of how pheremone concentration can affect a termite egg developing into a soldier. Once attractors are established, differentiation should cease and growth should proceed along these attractors. It is possible that a wasp making a home on a leaf disturbs the attractors of the leaf using chemical signals so that the growth pattern is modified to provide a home for the wasp.

Morphogenesis itself may be thought of as happening in a universe or world of its own, where different kinds of attactors are generated by genomes for selection. These are attractors (patterns of signalling) that result in cell specialization and function. This is indeed a natural selection process since the outcome of any single morphogenesis has to function in its environment. Whatever evolutionary strategies are developed outside the world of morphogenesis, it must be discovered first within, albeit in an indirect way. Thus true evolution may be said to take place within this world, though it is not directly in touch with the real world where form and function are selected for. This is basically developing "innate" knowledge of an external environment, as suggested in figure 5. Many systems that undergo morphogenesis gain indirect "knowledge" of one another. In this light, we may regard morphogenesis as perhaps the most complex process taking place on the planet.

In summary, we may hypothesize that in any scenario where sets of logic systems evolve against a collective logic, in addition to innate knowledge, self-referentiality as well as specialization will come into existence. This is true for cells, organisms and the human society (social, cultural and technological) - but at various levels of coupling.

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In conclusion, we suggest that there is a drive towards the discovery and assimilation of laws and correlations of nature into the genetic makeup of organisms. We would say that organisms of today are extremely hi-tech. And we shouldn't be too surprised if they are even using quantum effects to their advantage!. We have attempted to provide some explanation of how event sequences of behavior may be internalized over evolutionary time. The principles underlying genetic algorithms are nothing new, and their potential as optimizers is well known. In this sense, we have not suggested any new unknown principle at work, but have attempted to view and highlight its workings from a different perspective.

In our discussions here, we have not considered the issue of co-evolution of the environment logic with the creature-logic systems. We also have not considered "learning" during lifetime and its effects over evolutionary time (Baldwin effect) in moulding behavior [1]. Here we are more interested in inherited behavior as it is transmitted "blindly" without lifetime learning, though acquired behavior is also expected to come from discovery of domino event chains. For example, in forests, monkeys set out in groups for feeding destinations, leaving a flutter of insects in their path. Birds learn to follow these groups, dining on the insects they scatter. However, such chains may not be persistent over evolutionary time for genetic assimilation. Evolution of parental imitation would help, though.

We also have not attempted to separate out self-organization and emergent behavior from the event chains and their outcomes. It is expected that in a complex interconnected system like nature, almost every event has some immediate effect on the environment which leads to some form of emergent effect through the working of some natural law. Therefore many an observable functionality of organisms would involve self-organization and emergent behavior at various levels (molecular, biochemical, physical, ecological, to even astronomical).

It would be interesting to see whether a simulation study of evolution in the logic model suggested here, with random creature logic networks evolving against a sufficiently complex environment logic, would result in patterns of interactions like distributed control, attractors, symbiosis, parasitism etc. This is an avenue for further research.

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[1] Baldwin J.M. (1896), A new factor in evolution, American Naturalist, 30, 441-451
[2] Eldredge, N., Gould S.J. (1972), Punctuated equilibria: an alternative to phyletic gradualism. Models in Paleontology, ed. T.J.M. Schopf, pp. 82-115. San Francisco: Freeman, Cooper & Co.
[3] Hinde, R.A. (1982), Ethology, ed. Frank Kermode, William Collins Sons & Co. Ltd, Glasgow.
[4] Lehninger L.A.,., Nelson L.D., Cox M.M (1993), Principles of Biochemistry, Worth Publishers

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