Exploration and exploitation are the two cornerstones of problem solving by search. Evolutionary Algorithms (EAs) are search algorithms that explore the search space by the genetic search operators , while exploitation is done by selection. During the history of EAs diierent operators have emerged, mimicing asexual and sexual reproduction in Nature. Here we give an overview of the variety of th...

The application of a reasonable selection scheme plays an essential role in virtually all evolutionary algorithms. By not considering less-fit individuals, however, the algorithm discards valuable information about the fitness function. This paper explores to which extent a hybrid method, the evolutionary-gradient-search procedure, that applies a global operator to all offspring can be benefici...

This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and multiple human face detection method based on local gen...

One natural question when testing performance of global optimization algorithm is: how performances compare to a restart local search algorithm. One purpose of this paper is to provide results for such comparisons. To this end, the performances of a restart (advanced) local-search strategy, the CMA-ES with small initial step-size, are investigated on the 25 functions of the CEC 2005 real-parame...

Results from the artificial life community show that under some conditions evolving populations converge on broader, but less fit peaks in the fitness landscape and avoid more fit, but narrower peaks. Results from the evolutionary computation community show that over time genotypes evolve to become more resilient, where resiliency (or genetic robustness) is defined as the ability of an individu...

In this paper we describe niche search, a genetic-based opti-misation approach which is characterised by an evolutionary search on two layers: the individual layer (which is comparable to search described in other genetic algorithms), and the niche layer. Neither of these searches is directed: both individuals and niches evolve based on the selection of the ttest. The numerical results obtained...