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Memetic Multi-Agent Computing in Difficult Continuous Optimisation
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In the paper an application of hybridized Evolutionary Multi-Agent System (EMAS) with local search (in memetic style) to the problem of continuous optimisation is presented. Before, the concept of evolutionary and memetic agent-based computing is given, the former being a computing paradigm researched for over 15 years, the latter being introduced recently. Two ways of memetic hybridization (Lamarckian and Baldwinian) are discussed, and examined in the course of experiments. In the presented experiments, evolutionary and memetic multi-agent systems are compared with classical evolutionary algorithm (Michalewicz model) implemented with allopatric speciation (island-model of evolutionary algorithm), based on a selected popular benchmark continuous optimization functions.
Title: Memetic Multi-Agent Computing in Difficult Continuous Optimisation
Description:
In the paper an application of hybridized Evolutionary Multi-Agent System (EMAS) with local search (in memetic style) to the problem of continuous optimisation is presented.
Before, the concept of evolutionary and memetic agent-based computing is given, the former being a computing paradigm researched for over 15 years, the latter being introduced recently.
Two ways of memetic hybridization (Lamarckian and Baldwinian) are discussed, and examined in the course of experiments.
In the presented experiments, evolutionary and memetic multi-agent systems are compared with classical evolutionary algorithm (Michalewicz model) implemented with allopatric speciation (island-model of evolutionary algorithm), based on a selected popular benchmark continuous optimization functions.
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