Co-evolutionary learning on noisy tasks

The paper studies the effect of noise on co-evolutionary learning, using Backgammon as a typical noisy task. It might seem that co-evolutionary learning would be ill-suited to noisy tasks: genetic drift causes convergence to a population of similar individuals, and on noisy tasks it would seem to require many samples (i.e., many evaluations and long computation time) to discern small differences between similar population members. Surprisingly, the paper learns otherwise: for small population sizes, the number of evaluations does have an effect on learning; but for sufficiently large populations, more evaluations do not improve learning at all-population size is the dominant variable. This is because a large population maintains more diversity, so that the larger differences in ability can be discerned with a modest number of evaluations. This counter-intuitive result means that co-evolutionary learning is a feasible method for noisy tasks, such as military situations and investment management

On Evolving Robust Strategies for Iterated Prisoner's Dilemma

Evolution is a fundamental form of adaptation in a dynamic and complex environment. Genetic algorithms are an effective tool in the empirical study of evolution. This paper follows Axelrod's work [2] in using the genetic algorithm to evolve strategies for playing the game of Iterated Prisoner's Dilemma, using co-evolution, where each member of the population (each strategy) is evaluated by how it performs against the other members of the current population. This creates a dynamic environment in which the algorithm is optimising to a moving target instead of the usual evaluation against some fixed set of strategies. The hope is that this will stimulate an "arms race" of innovation [3]. We conduct two sets of experiments. The first set investigates what conditions evolve the best strategies. The second set studies the robustness of the strategies thus evolved, that is, are the strategies useful only in the round robin of its population or are they effective against a wide variety of oppo..

Viability of populations in a landscape

Darwen, P. J. and Green, D. G., Viability of populations in

The Exploitation of Cooperation in Iterated Prisoner's Dilemma

We follow Axelrod [2] in using the genetic algorithm to play Iterated Prisoner's Dilemma. Each member of the population (i.e., each strategy) is evaluated by how it performs against the other members of the current population. This creates a dynamic environment in which the algorithm is optimising to a moving target instead of the usual evaluation against some fixed set of strategies, causing an "arms race" of innovation [3]. We conduct two sets of experiments. The first set investigates what conditions evolve the best strategies. The second set studies the robustness of the strategies thus evolved, that is, are the strategies useful only in the round robin of its population or are they effective against a wide variety of opponents? Our results indicate that the population has nearly always converged by about 250 generations, by which time the bias in the population has almost always stabilised at 85%. Our results confirm that cooperation almost always becomes the dominant strategy [1,..

Robust and Efficient Genetic Algorithms with Hierarchical Niching and a Sustainable Evolutionary Computation Model

Abstract. This paper proposes a new niching method named hierarchical niching, which combines spatial niching in search space and a continuous temporal niching concept. The method is naturally implemented as a new genetic algorithm, QHFC, under a sustainable evolutionary computation model: the Hierarchical Fair Competition (HFC) Model. By combining the benefits of the temporally continuing search capability of HFC and this spatial niching capability, QHFC is able to achieve much better performance than deterministic crowding and restricted tournament selection in terms of robustness, efficiency, and scalability, simultaneously, as demonstrated using three massively multimodal benchmark problems. HFC-based genetic algorithms with hierarchical niching seem to be very promising for solving difficult real-world problems.