Ecosystem-Oriented Distributed Evolutionary Computing

We create a novel optimisation technique inspired by natural ecosystems, where the optimisation works at two levels: a first optimisation, migration of genes which are distributed in a peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We consider from the domain of computer science distributed evolutionary computing, with the relevant theory from the domain of theoretical biology, including the fields of evolutionary and ecological theory, the topological structure of ecosystems, and evolutionary processes within distributed environments. We then define ecosystem- oriented distributed evolutionary computing, imbibed with the properties of self-organisation, scalability and sustainability from natural ecosystems, including a novel form of distributed evolu- tionary computing. Finally, we conclude with a discussion of the apparent compromises resulting from the hybrid model created, such as the network topology.Comment: 8 pages, 5 figures. arXiv admin note: text overlap with arXiv:1112.0204, arXiv:0712.4159, arXiv:0712.4153, arXiv:0712.4102, arXiv:0910.067

Ecology-inspired evolutionary algorithm using feasibility-based grouping for constrained optimization, in:

Abstract Different strategies for defining the relationship between feasible and infeasible individuals in evolutionary algorithms can provide with very different results when solving numerical constrained optimization problems. This paper proposes a novel EA to balance the relationship between feasible and infeasible individuals to solve numerical constrained optimization problems. According to the feasibility of the individuals, the population is divided into two groups, feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. Parents for reproduction are selected from the two groups by a novel parent selection method. The proposed method is tested using (l, k) evolution strategies with 13 benchmark problems. The results show that the proposed method improves the searching performance for most of the tested problems