Evolutionary multi-agent systems

Aleksander Byrski, Rafał Dreżewski, Leszek Siwik, Marek Kisiel-Dorohinicki
2015 Knowledge engineering review (Print)  
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 lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist
more » ... variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results. Such a system consists of a relatively large number of rather simple (reactive), often homogeneous agents, which possess or produce solutions to the same problem (a common goal), it is rather closed and static, but non-determinitic (Moya & Tolk, 2007) . Because of both computational simplicity and a huge number of the agents, the influence of each single agent's behaviour on the overall system operation may be neglected, which allows for the efficient realisation in large-scale environments with lightweight infrastructure (Byrski et al., 2012) . The article aims to summarise research done for 15 years since the idea of EMAS was proposed by Cetnarowicz (1996). First, the background information and general structure of EMAS are presented at the beginning of the paper. Next, a basic model of EMAS and several of its variations (immunological, elitist, co-evolutionary) are described along with applications in different optimisation domains (global, multicriteria and multi-modal). Each application is illustrated with selected experimental results recalled from the authors' publications. References show both the most important contribution of the authors and their collaborators in the field, as well as state-of-the-art that help to locate the presented work against the background of population-based metaheuristics. Towards decentralised evolutionary computation Evolutionary computation-a heuristic problem-solving approach based on models of organic evolutionhas been successfully used for difficult optimisation problems for over 40 years (Fogel, 1998; de Jong, 2002; de Castro, 2006) . This approach is particularly useful when classical computational methods turn out to be ineffective, because corresponding models are too complex, or formulas applied too complicated, or even some formulations must be rejected in the face of numerical instability of available solvers. Unfortunately, there is no guarantee to find satisfactory solutions by evolutionary algorithms, and their performance aspects for particular problems must be verified empirically. This is simply because theoretical analyses make a number of assumptions in regard to the algorithm which limit their validity in real-world scenarios. Yet, an evolutionary algorithm may often give an approximate solution with controllable adequacy, which means that a solving process can be stopped by a decision maker anytime he is satisfied. Practice proves that an evolutionary algorithm works properly (e.g. in terms of searching for a globally optimal solution) if the population consists of fairly different individuals, that is, the so-called diversity in the population is preserved (Bäck et al., 1997 ). Yet, many algorithms tend to prematurely loose this useful diversity and therefore the risk arises that the population might get stuck in some part of the search space (e.g. in the basin of attraction of a certain local extremum instead of searching for a global one). Loosing the population diversity also limits the possibilities of their application in some areas such as multiobjective optimisation or multi-modal optimisation. The situation described above may be related to the fact that the model of evolution employed by simple evolutionary algorithms lacks many important features observed in organic evolution (Back et al., 1997) . This includes dynamically changing environmental conditions, lack of global knowledge, no generational synchronisation, co-evolution of species, evolving genotype-phenotype mapping, etc. That is why many variations of classical evolutionary algorithms were proposed, introducing additional mechanisms following the most important phenomena in evolutionary biology. Among these, decomposition and coevolutionary approaches had the most inspiring effect on the genesis of EMAS. Decomposition and co-evolutionary techniques Niching (or speciation) techniques (Mahfoud, 1995) are aimed at introducing useful population diversity by forming sub-populations (species). Allopatric (or geographic) speciation occurs when individuals of the same species become isolated because of geographical or social changes. Decomposition approaches of the so-called PEA model such phenomena by introducing non-global selection/mating and some spatial structure of population (Cantú-Paz, 1995) . In a coarse-grained PEA (also known as regional or multiple deme model), the population is divided into several sub-populations (regions, demes), selection/mating is limited to individuals inhabiting one region and a migration operator is used to move (copy) selected individuals from one region to another.
doi:10.1017/s0269888914000289 fatcat:p55jq657hjhczp7sht2r7wavju