Memetic Computing In Selected Agent-Based Evolutionary Systems

Abstract

In the paper an application of selected agent-based evolutionary computing models, such as flock-based multi agent system (FLOCK) and evolutionary multi-agent system (EMAS), to the problem of continuous optimisation is presented. It turns out, that hybridizing of agent-based paradigm with evolutionary computation brings a new quality to the meta-heuristic field, easily enhancing static individuals with possibilities of perception and interaction with other agents. The examination of selected benchmarks leads to the observation regarding the overall efficiency of the systems in comparison to the standard genetic algorithm (as defined by Michalewicz) and memetic versions of all the systems. The experiments confirm that the efficiency is dependent on the problem, however, the observed number of fitness function calls makes EMAS dominate over its competitors. This feature makes EMAS a promising solution for the problems with complex fitness functions, (such as inverse problems).