On the Combination of Coevolution and Novelty Search

This paper develops a new method for coevolution, named Fitness-Diversity Driven Coevolution (FDDC). This approach builds on existing methods by a combination of a (predator-prey) Coevolutionary Genetic Algorithm (CGA) and novelty search. The innovation lies in replacing the absolute novelty measure with a relative one, called Fitness-Diversity. FDDC overcomes problems common in both CGAs (premature convergence and unbalanced coevolution) and in novelty search (construction of an archive). As a proof of principle, Spring Loaded Inverted Pendulums (SLIPs) are coevolved with 2Dterrains the SLIPs must learn to traverse

Coevolutionary Life-time Learning

. This work studies the interaction of evolution and learning. It starts from the coevolutionary genetic algorithm (CGA) introduced earlier. Two techniques - life-time fitness evaluation (LTFE) and predator-prey coevolution - boost the genetic search of a CGA. The partial but continuous nature of LTFE allows for an elegant incorporation of life-time learning (LTL) within CGAs. This way, not only the genetic search but also the LTL component focuses on "not yet solved" problems. The performance of the new algorithm is compared with various other algorithms. 1 Introduction The combination of evolutionary learning and life-time learning (LTL) in genetic algorithms (GAs) is an active field of research nowadays. The rise of interest in the combination of both types of learning has several reasons. First of all, it is clear that nature combines both types of learning. Hence, it is of interest to gather a better understanding of the interaction between, and the advantages of, both types of l..

Coevolutionary Computation

This paper proposes a general framework for the use of coevolution to boost the performance of genetic search. It combines coevolution with yet another biologically inspired technique, called life-time fitness evaluation (LTFE). Two unrelated problems - neural net learning and constraint satisfaction - are used to illustrate the approach. Both problems use predator-prey interactions to boost the search. In contrast with traditional "single population " genetic algorithms (GAs), two populations constantly interact and coevolve. However, the same algorithm can also be used with different types of coevolutionary interactions. As an example, the symbiotic coevolution of solutions and genetic representations is shown to provide an elegant solution to the problem of finding a suitable genetic representation. The approach presented here greatly profits from the partial and continuous nature of LTFE. Noise tolerance is one advantage. Even more important, LTFE is ideally suited to deal with co..

Coevolving Cellular Automata: Be Aware of the Red Queen!

This paper studies the use of coevolution to search for a cellular automaton (CA) solving the well-known density classification task. The Coevolutionary Genetic Algorithm (CGA) coevolves two non-interbreeding populations which interact as predator and prey. The main purpose of this paper is to illustrate some of the intricacies involved in the use of coevolution to solve a given task. Concepts from standard GA theory can be used to understand these problems. A simple modification is proposed to significantly increase the performance. 1 INTRODUCTION In nature, predator-prey interactions provide a driving force towards complexity. This because there is a strong evolutionary pressure for prey to defend themselves better (e.g. by running quicker, growing bigger shields, better camouflage ...) in response to which future generations of predators develop better attacking strategies (e.g. stronger claws, better eye-sight ...). Such arms races are characterized by an inverse fitness interactio..