This paper presents a brief overview of the eld of evolutionary computation. Three major research areas of evolutionary computation will be discussed; evolutionary computation theory, evolutionary optimisation and evolutionary learning. The state-of-the-art and open issues in each area will be addressed. It is indicated that while evolutionary computation techniques have enjoyed great success i...

Evolutionary computation is an area of computer science that uses ideas from biological evolution to solve computational problems. Many such problems require searching through a huge space of possibilities for solutions, such as among a vast number of possible hardware circuit layouts for a configuration that produces desired behavior, for a set of equations that will predict the ups and downs ...

Evolutionary Computation Applied to the Control of Sound Synthesis

This paper presents a multiobjetive approach to solve the Linear Shelf Space Allocation Problem (LiSSAP), which consists on allocating lengths of shelves in a given shop to specific products or groups of products. Previously we gave the first steps towards the development of a commercially viable tool that used evolutionary computation to address the problem; in this paper we introduce MELiSSA,...

Parameter control is still one of the main challenges in evolutionary computation. This paper is concerned with controlling selection operators on-the-fly. We perform an experimental comparison of such methods on three groups of test functions and conclude that varying selection pressure during a GA run often yields performance benefits, and therefore is a recommended option for designers and u...

The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and nite time behavior of evolutionary algorithms with nite search spaces and discrete time scale. Results on evolutionary algorithms beyond nite space and discrete time are also presented but with reduced elaboration.

One application of evolutionary computation is developing agents which play in team-based games. In such games, it is often beneficial for each agent in a team to specialise in a different role within the team. We present several methods of evolving such specialisation by assessing agents as members of teams, and compare their performance in evolving solutions to a simulation of an immune syste...