How Learning Can Guide Evolution

Well, this paper gave me lot of boost! An old paper from 1987 but a gem! 

Abstract. 

The assumption that acquired characteristics are not inherited is often taken to imply that the adaptations that an organism learns during its lifetime cannot guide the course of evolution. This inference is incorrect (Baldwin, 1896). Learning alters the shape of the search space in which evolution operates and thereby provides good evolutionary paths towards sets of co-adapted alleles. We demonstrate that this effect allows learning organisms to evolve much faster than their nonlearning equivalents, even though the characteristics acquired by the phenotype are not communicated to the genotype.

Discussion

The most common argument in favor of learning is that some aspects of the environment are unpredictable, so it is positively advantageous to leave some decisions to learning rather than specifying them genetically (e.g. Harley, 1981). This argument is clearly correct and is one good reason for having a learning mechanism, but it is different from the Baldwin effect which applies to complex co-adaptations to predictable aspects of the environment.

To keep the argument simple, we started by assuming that learning was simply a random search through possible switch settings. When there is a single good combination and all other combinations are equally bad a random search is a reasonable strategy, but for most learning tasks there is more structure than this and the learning process should make use of the structure to home in on good switch configurations. More sophisticated learning procedures could be used in these cases (e.g. Rumelhart, Hinton, and Williams, 1986). Indeed, using a hillclimbing procedure as an inner loop to guide a genetic search can be very effective (Brady, 1985). As Holland (1975) has shown, genetic search is particularly good at obtaining evidence about what confers fitness from widely separated points in the search space. Hillclimbing, on the other hand, is good at local, myopic optimization. When the two techniques are combined, they often perform much better than either technique alone (Ackley, 1987). Thus, using a more sophisticated learning procedure only strengthens the argument for the importance of the Baldwin effect.

For simplicity, we assumed that the learning operates on exactly the same variables as the genetic search. This is not necessary for the argument. Each gene could influence the probabilities of large numbers of potential connections and the learning would still improve the evolutionary path for the genetic search. In this more general case, any Lamarckian attempt to inherit acquired characteristics would run into a severe computational difficulty: To know how to change the genotype in order to generate the acquired characteristics of the phenotype it is necessary to invert the forward function that maps from genotypes, via the processes of development and learning, to adapted phenotypes. This is generally a very complicated, non-linear, stochastic function and so it is very hard to compute how to change the genes to achieve desired changes in the phenotypes even when these desired changes are known.

We have focused on the interaction between evolution and learning, but the same combinatorial argument can be applied to the interaction between evolution and development. Instead of directly specifying the phenotype, the genes could specify the ingredients of an adaptive process and leave it to this process to achieve the required end result. An interesting model of this kind of adaptive process is described by Von der Malsburg and Willshaw (1977). Waddington (1942) suggested this type of mechanism to account for the inheritance of acquired characteristics within a Darwinian framework. There is selective pressure for genes which facilitate the development of certain useful characteristics in response to the environment. In the limit, the developmental process becomes canalized: The same characteristic will tend to develop regardless of the environmental factors that originally controlled it. Environmental control of the process is supplanted by internal genetic control. Thus, we have a mechanism which as evolution progresses allows some aspects of the phenotype that were initially specified indirectly via an adaptive process to become more directly specified.

Our simulation supports the arguments of Baldwin and Waddington, and demonstrates that adaptive processes within the organism can be very effective in guiding evolution. The main limitation of the Baldwin effect is that it is only effective in spaces that would be hard to search without an adaptive process to restructure the space. The example we used in which there is a single spike of added fitness is clearly an extreme case, and it is difficult to assess the shape that real evolutionary search spaces would have if there were no adaptive processes to restructure them. It may be possible to throw some light on this issue by using computer simulations to explore the shape of the evolutionary search space for simple neural networks that do not learn, but such simulations always contain so many simplifying assumptions that it is hard to assess their biological relevance. We therefore conclude with a disjunction: For biologists who believe that evolutionary search spaces contain nice hills (even without the restructuring caused by adaptive processes) the Baldwin effect is of little interest,[3] but for biologists who are suspicious of the assertion that the natural search spaces are so nicely structured, the Baldwin effect is an important mechanism that allows adaptive processes within the organism to greatly improve the space in which it evolves.