evolutionary paradigms that will enable such subcomponents to “emerge ” rather than being hand designed. In this paper, we describe an architecture for evolving such subcomponents as a collection of cooperating species. Given a simple stringmatching task, we show that evolutionary pressure to increase

evolutionary paradigms that will enable such subcomponents to "emerge" rather than being hand designed. In this paper we describe an architecture for evolving such subcomponents as a collection of cooperating species. Given a simple string-matching task, we show that evolutionary pressure

. The task of the AEP is to create a Connectivity Matrix (CM) which can be transformed into the ANN of any architecture. To create AEPs, and in consequence ANNs, genetic algorithms (GAs) are used. In addition to the outline of AE, the paper also presents a new AEP encoding method, i.e., the method used

Cooperative Coevolution (CC) has been shown to be effective in problems where certain architectural details of the solution are evolved. This is the case of cascade neural networks where the number of hidden units is not preestablished but rather emerges through learning. We take a step towards

. We both examine the on-line adaption of behavioral strategies utilizing genetic programming and demonstrate the successful co-evolution of cooperative individuals. We present a new crossover mechanism for genetic programming systems in order to facilitate the evolution of more than one member

testing this architecture in several problem domains is presented which shows a significant speedup over more traditional non-coevolutionary approaches. 1 INTRODUCTION For both natural and artificial systems the ability to evolve complex structures is desirable, but difficult to achieve. Our

Cooperative coevolution algorithms (CCEAs) evolve solu-tions that consist of interacting, coadapted components [5]. CCEAs are capable of evolving a heterogeneous set of coop-erating agent behaviours, where each agent can have a spe-

Cooperative coevolution is an approach for evolving solu-tions composed of coadapted components. Previous research has shown, however, that cooperative coevolutionary algo-rithms are biased towards stability: they tend to converge prematurely to equilibrium states, instead of converging to optimal

Abstract—Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems

In nature there are teams of collaborators and competitors that evolve at the same time, yet computationally they have mostly been studied separately so far. This paper focuses on simultaneous cooperative and competitive coevolution in a complex predator-prey domain. Yong and Miikkulainen’s [3