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 2D-terrains the SLIPs must learn to traverse.

This paper investigates the evolution of two types of simple Genotype Phenotype Mappings (GPMs): a many-to-one mapping and a one-to-many mapping. Both GPMs are under genetic control. For both types of mappings different Regions Of Maximum Adaptability (ROMAs) are found. These ROMAs are the regions - in a paramterized space of GPMs - evolution leads to. The attraction towards these ROMAs increases as selection pressure increases. Finally, this paper discusses the evolution of pleiotropy and the ROMAs it leads to.

This paper investigates the dynamics of a simple coevolutionary system. It consists of a predator-prey system in which one population maximizes its distance to the members of the other population, while the second population tries to minimize the distance to the first population. This results in a coevolutionary pursuer-evader (PE) system whose dynamics can easily be visualised and studied.

This article 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 lifetime 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 co-evolve. However, the same algorithm can also be used with different types of co-evolutionary 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 coupled fitness landscapes typical for coevolution.