Titlebook: Artificial Evolution; 15th International C Pierrick Legrand,Arnaud Liefooghe,Evelyne Lutton Conference proceedings 2023 The Editor(s) (if a

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0302-9743 e range of topics in the field of artificial evolution, including, but not limited to: evolutionary?computation, evolutionary optimization, coevolution, artificial life, population dynamics, theory, algorithmic and?modeling, implementations..978-3-031-42615-5978-3-031-42616-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Constitution

Gary Van Zant,Eugene Goldwasser Then, we propose a linear programming model, heuristic, and a metaheuristic combining a simulated annealing algorithm with an iterated local search procedure to solve it. We provide computational results to show the efficiency of the proposed methods.

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Regulation of Stem Cell Proliferationtion of . in terms of the electric bus scheduling problem noting that quality is difficult to quantify. A range of quality metrics are presented that can be used with differing effectiveness to optimally schedule electric buses.

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,On the?Active Use of?an?ND-Tree-Based Archive for?Multi-Objective Optimisation,rformance changes these cause (and discuss their drivers). Illustrations are provided with data sequences from a tunable generator and also a simple evolution strategy — but we emphasise such data structures are optimisation algorithm agnostic, and may be effectively integrated across the range of evolutionary (and non-evolutionary) optimisers.

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泥瓦匠

S. Orbach-Arbouys,M. Castes,B. M. Berardeta properties. A new selection mechanism is proposed for the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) in order to approximate equilibria at each node level. Numerical experiments illustrate the behavior of the approach compared with other decision trees based methods.

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