Evolving Agents in a Market Simulation Platform ~ A Test for Distinct Meta-Heuristics

Oo, Naing Win; Miranda, Vladimiro

doi:10.1109/isap.2005.1599311

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…One must refer that in [8] the authors claimed to have a self-adapting process for this parameter; however, its value would only change with a certain probability (0.1), remaining fixed most of the time, so it must be seen as a quite modest effort into selfadaptation. But the EPSO scheme is truly self-adaptive, so it is natural to wonder if the EPSO scheme would not work also when acting over the DE parameter.…”

Section: The Deepso Alternativementioning

confidence: 99%

Differential Evolutionary Particle Swarm Optimization (DEEPSO): A Successful Hybrid

Miranda

Alves

2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.

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Section: The Deepso Alternativementioning

confidence: 99%