Aarhus University

Computer Science Department

NyMunkegade Bldg. 504

DK-8000 Aarhus C, Denmark

Phone: +45 8942 3373; Fax: +45 8942 3255

www.daimi.au.dk/~brian/cec99.html

1. "On a Coevolutionary Genetic Algorithm for Constrained Optimization"

Helio Barbosa (LNCC.Petropolis,Brazil)

hcbm@fluidyn.lncc.br

2. "Curiosity through cooperating/competing algorithmic predictors"

Jurgen Schmidhuber(IDSIA,Lugano,Switzerland)

juergen@idsia.ch

3. "Learning Nash Equilibria by Coevolving Distributed Classifier Systems"

F. Seredynski (IPIPAN,Warsaw,Poland)

sered@ipipan.waw.pl

Cezary Z. Janikow

4. On preliminary studies of learning variable interdependencies in coevolutionary optimizers"

Karsten & Nicole Weicker (Un.Tubingen,Un.Stuttgart,Germany)

weicker@Informatik.Uni-Tuebingen.De

5. "Multinational evolutionary algorithms"

Rasmus K. Ursem

Since practical problems often are very complex with a large number of objectives it can be difficult or impossible to create an objective function expressing all the criterias of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting. Because evolutionary algorithms are population-based they have the best potential for finding more of the best solutions among the possible solutions. However, standard EAs often converge to one solution and leave therefore only this single option for a final human selection. So far at least two methods, sharing and tagging, have been proposed to solve the problem. This paper presents a new method for finding more quality solutions, not only global optimas but local as well. The method tries to adapt it's search strategy to the problem by taking the topology of the fitness landscape into account. The idea is to use the topology of the fitness landscape to group the individuals into sub-populations each covering a part of the fitness landscape.