Artificial Evolution

This book constitutes selected best papers from the 10th International Conference on Artificial Evolution, EA 2011, held in Angers, France, in October 2011.
Initially, 33 full papers and 10 post papers were carefully reviewed and selected from 64 submissions. This book presents the 19 best papers selected from these contributions. The papers are organized in topical sections on ant colony optimization; multi-objective optimization; analysis; implementation and robotics; combinatorial optimization; learning and parameter tuning; new nature inspired models; probabilistic algorithms; theory and evolutionary search; and applications.

Inhalt

Ant Colony Optimization.- An Immigrants Scheme Based on Environmental Information for Ant Colony Optimization for the Dynamic Travelling Salesman Problem.- Multi Objective Optimization.- A Surrogate-Based Intelligent Variation Operator for Multiobjective Optimization.- The Relationship between the Covered Fraction, Completeness and Hypervolume Indicators.- Analysis A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize.- Local Optima Networks with Escape Edges.- Visual Analysis of Population Scatterplots.- Implementation and Robotics An On-Line On-Board Distributed Algorithm for Evolutionary Robotics.- Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm.- Two Ports of a Full Evolutionary Algorithm onto GPGPU.- Combinatorial Optimization.- A Multilevel Tabu Search with Backtracking for Exploring Weak Schur Numbers.- An Improved Memetic Algorithm for the Antibandwidth Problem.- Learning and Parameter Tuning Adaptive Play in a Pollution Bargaining Game.- Learn-and-Optimize: A Parameter Tuning Framework for Evolutionary AI Planning.- New Nature Inspired Models.- A Model Based on Biological Invasions for Island Evolutionary Algorithms.- A Multi-objective Particle Swarm Optimizer Enhanced with a Differential Evolution Scheme.- Probabilistic Algorithms Evolution of Multisensory Integration in Large Neural Fields.- Reducing the Learning Time of Tetris in Evolution Strategies.- Theory and Evolutionary Search.- Black-Box Complexity: Breaking the O(n log n) Barrier of LeadingOnes.- Applications.- Imperialist Competitive Algorithm for Dynamic Optimization of Economic Dispatch in Power Systems.