Titlebook: Artificial Evolution; 12th International C Stéphane Bonnevay,Pierrick Legrand,Marc Schoenauer Conference proceedings 2016 Springer Internat

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tions of other non-terminals, thereby increasing locality. The performance of the new representation is accessed on a set of benchmark problems. The results obtained confirm the effectiveness of the proposed approach, as it is able to outperform standard grammatical evolution on all selected optimization problems.

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Lymphocyte

Approaches for Many-Objective Optimization: Analysis and Comparison on MNK-Landscapes,landscapes to trace the dynamics of the algorithms generating high-resolution approximations of the Pareto optimal set. Also, we use large MNK-landscapes to analyze their scalability to larger search spaces.

Infirm

Quasi-random Numbers Improve the CMA-ES on the BBOB Testbed,ts provide a significant improvement in evolutionary optimization. In this paper, we experiment quasi-random mutations on a well known test case, namely the Coco/Bbob test case. We also include experiments on translated or rescaled versions of BBOB, on which we get similar improvements.

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geometrician

A Distributed Hybrid Algorithm for the Graph Coloring Problem,ypes of perturbation moves are performed. All these search components are managed by a multi-agent model which uses reinforcement learning for decision making. The performance of the proposed algorithm is evaluated on well-known DIMACS benchmark instances.

Indigence

Traffic Signal Optimization: Minimizing Travel Time and Fuel Consumption,the nature and the extent of the conflict between these objectives. We also compare with a single-objective optimization algorithm where only travel time is optimized and evaluate the impact of the signals settings on gas emissions.

Eulogy

How to Mislead an Evolutionary Algorithm Using Global Sensitivity Analysis,y exploiting information gathered from GSA might not be so straightforward. In this paper, we present three mono- and multi-objective counterexamples to prove how naively combining GSA and EA may mislead an optimisation process.

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0302-9743 nd Modeling,Implementations, Application of Evolutionary Paradigms to the Real World,Dynamic Optimization, Machine Learning and hybridization with other softcomputing techniques..978-3-319-31470-9978-3-319-31471-6Series ISSN 0302-9743 Series E-ISSN 1611-3349