Leszek Siwik

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Evolutionary algorithms are (meta-)heuristic techniques used in the case of search, optimization, and adaptation problems, which cannot be solved with the use of traditional methods. Sexual selection mechanism helps to maintain the population diversity in evolutionary algorithms. In this paper the agent-based realization of multi-objective evolutionary(More)
Co-evolutionary techniques for evolutionary algorithms are aimed at overcoming their limited adaptive capabilities and allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. In this paper the idea of co-evolutionary multi-agent system with host-parasite mechanism for(More)
Co-evolutionary techniques for evolutionary algorithms are aimed at overcoming their limited adaptive capabilities and allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. Sexual selection resulting from sexual conflict and co-evolution of female mate choice and male(More)
Abstract. Co-evolutionary techniques for evolutionary algorithms help overcoming limited adaptive capabilities of evolutionary algorithms, and maintaining population diversity. In this paper the idea and formal model of agent-based realization of predator-prey co-evolutionary algorithm is presented. The presented system is applied to the problem of(More)
The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at(More)
Abstract. Co-evolutionary techniques for evolutionary algorithms help overcoming limited adaptive capabilities of evolutionary algorithms, and maintaining population diversity. In this paper the idea and formal model of agent-based realization of predator-prey co-evolutionary algorithm is presented. The effect of using such approach is not only the location(More)
The complexity of generating investment strategies makes it hard (or even impossible), in most cases, to use traditional techniques and to find the exact solution. In this chapter the evolutionary system for generating investment strategies is presented. The algorithms used in the system (evolutionary algorithm, co-evolutionary algorithm, and agent-based(More)
Co-evolutionary algorithms are a special type of evolutionary algorithms, in which the fitness of each individual depends on other individuals’ fitness. Such algorithms are applicable in the case of problems for which the formulation of explicit fitness function is difficult or impossible. Co-evolutionary algorithms also maintain population diversity better(More)
Introducing elitism into evolutionary multi-agent system for multi-objective optimization proofed to be smooth both conceptually and in realization. Simultaneously it allowed for obtaining results with comparable high quality to such referenced algorithms as Non-dominated Sorting Genetic Algorithm (NSGA-II) or Strength Pareto Evolutionary Algorithm (SPEA2).(More)