Coevolutionary Dynamics in a Minimal Substrate

One of the central difficulties of coevolutionary methods arises from 'intransitive superiority' - in a two-player game, for example, the fact that A beats B, and B beats C, does not exclude the possibility that C beats A. Such cyclic superiority in a coevolutionary substrate is hypothesized to cause cycles in the dynamics of the population such that it 'chases its own tail' - traveling through some part of strategy space more than once despite apparent improvement with each step. It is often difficult to know whether an application domain contains such difficulties and to verify this hypothesis in the failure of a given coevolutionary set-up. In this paper we wish to elucidate some of the issues and concepts in an abstract domain where the dynamics of coevolution can be studied simply and directly. We define three simple 'number games' that illustrate intransitive superiority and resultant oscillatory dynamics, as well as some other relevant concepts. These include the distinction between a player's perceived performance and performance with respect to an external metric, and the significance of strategies with a multi-dimensional nature. These features alone can also cause oscillatory behavior and coevolutionary failure

Mutualism, Parasitism, and Evolutionary Adaptation

Our investigations concern the role of symbiosis as an enabling mechanism in evolutionary adaptation. Previous work has illustrated how the formation of mutualist groups can guide genetic variation so as to enable the evolution of ultimately independent organisms that would otherwise be unobtainable. The new experiments reported here show that this effect applies not just in genetically related organisms but may also occur from symbiosis between distinct species. In addition, a new detail is revealed: when the symbiotic group members are drawn from two separate species only one of these species achieves eventual independence and the other remains parasitic. It is nonetheless the case that this second species, formerly mutualistic, was critical in enabling the independence of the first. We offer a biological example that is suggestive of the effect and discuss the implications for evolving complex organisms, natural and artificial

Modeling Building Block Interdependency

The Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA test problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various test problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-IFF) as a canonical example. We present some empirical results of GAs on H-IFF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests

Theme preservation and the evolution of representation

Abstract. The identification of mechanisms by which constraints on phenotypic variability are tuned in nature, and the implementation of these mechanisms in Evolutionary Algorithms (EAs) carries the promise of making EAs less “wasteful”. The constraints on phenotypic variability are determined by the way genotypic variability maps to phenotypic variability. This in turn is determined by the way that phenotypes are represented genotypically. We use a formal model of an EA to show that when some part of the genome is mutated with a much lower probability than some other part, representations used to search the phenotype space- and hence the constraints on phenotypic variability- can themselves be thought to evolve. Specifically, we formally analyze a class of mutationonly fitness proportional evolutionary algorithms and show that these evolutionary algorithms implicitly implement what we call subrepresentation evolving multithreaded evolution. These EAs conduct second-order search over a predetermined set of representations and exploit promising representations within this set for first order evolutionary search. We compare our analytical method and results with those employed in schema analysis and note that by examining systems that are simpler than the ones examined in a typical schema analysis (mutation is the only variational operator in our systems), and by changing how we define the subsets of the genotype space that are analyzed, we have obtained results that are more intuitively understandable and are not specific to a particular data-structure. 1

Gene Regulatory Network Evolution Through Augmenting Topologies

International audienceArtificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm's use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments