Günther R. Raidl – författare

This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives.     The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.

This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives.     The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.

Evolutionary Computation (EC) involves the study of problem solving and op- mization techniques inspired by principles of natural evolution and genetics. EC has been able to draw the attention of an increasing number of researchers and practitioners in several ?elds. Evolutionary algorithms have in particular been showntobee?ectivefordi?cultcombinatorialoptimizationproblemsappearing in various industrial, economic, and scienti?c domains. This volume contains the proceedings of EvoCOP 2004, the 4th European ConferenceonEvolutionaryComputationinCombinatorialOptimization.Itwas held in Coimbra, Portugal, on April 5-7, 2004, jointly with EuroGP 2004, the 7th European Conference on Genetic Programming, and EvoWorkshops 2004, which consisted of the following six individual workshops: EvoBIO, the 2nd - ropean Workshop on Evolutionary Bioinformatics; EvoCOMNET, the 1st - ropean Workshop on Evolutionary Computation in Communications, Networks, and Connected Systems; EvoHOT, the 1st European Workshop on Hardware Optimisation; EvoIASP, the 6th European Workshop on Evolutionary Com- tation in Image Analysis and Signal Processing; EvoMUSART, the 2nd Eu- pean Workshop on Evolutionary Music and Art; and EvoSTOC, the 1st Eu- pean Workshop on Evolutionary Algorithms in Stochastic and Dynamic En- ronments.

This book constitutes the joint refereed proceedings of six workshops on evolutionary computing, EvoWorkshops 2004, held together with EuroGP 2004 and EvoCOP 2004 in Coimbra, Portugal, in April 2004. The 55 revised full papers presented were carefully reviewed and selected from a total of 123 submissions. In accordance with the six workshops covered, the papers are organized in topical sections on evolutionary bioinformatics; evolutionary computing in communications, networks, and connected systems; hardware optimization techniques; evolutionary computing in image analysis and signal processing; evolutionary music and art; and evolutionary algorithms in stochastic and dynamic environments.

Evolutionary computation (EC) involves the study of problem-solving and op- mization techniques inspired by principles of natural evolution and genetics. EC has been able to draw the attention of an increasing number of researchers and practitioners in several ?elds. Evolutionary algorithms have in particular been showntobee?ectivefordi?cultcombinatorialoptimizationproblemsappearing in various industrial, economics, and scienti?c domains. This volume contains the proceedings of EvoCOP 2005, the 5th European Conference on Evolutionary Computation in Combinatorial Optimization.It was held in Lausanne, Switzerland, on 30 March-1 April 2005, jointly with EuroGP 2005, the 8th European Conference on Genetic Programming, and the EvoWorkshops 2005, which consisted of the following six individual workshops: EvoBIO, the 3rd European Workshop on Evolutionary Bioinformatics; Evo- COMNET, the 2nd European Workshop on Evolutionary Computation in C- munication, Networks, and Connected Systems; EvoHOT, the 2nd European Workshop on Hardware Optimisation Techniques; EvoIASP, the 7th European Workshop on Evolutionary Computation in Image Analysis and Signal Proce- ing; EvoMUSART, the 3rd European Workshop on Evolutionary Music and Art; and EvoSTOC, the 2nd European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments.

This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2006, held in Budapest, Hungary in April 2006. The 24 revised full papers presented were carefully reviewed and selected from 77 submissions. The papers include coverage of evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, and memetic algorithms.

Evolutionary Computation (EC) is a rapidly expanding eld of computer science in which problem solving, optimization, and machine learning techniques ins- red by genetics and natural selection are studied. In recent years, a number of studies and results have been reported in the literature which have disclosed the potentials of EC techniques and shown their capability to solve hard problems in several domains. This volume contains the proceedings of EvoWorkshops 2001, an event - cluding the First European Workshop on Evolutionary Computation in Com- natorial Optimization (EvoCOP), the Second European Workshop on Evolut- nary Aeronautics (EvoFlight), the Third European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP), the First - ropean Workshop on Evolutionary Learning (EvoLearn), and the Second Eu- pean Workshop on Evolutionary Scheduling and Timetabling (EvoSTIM). These workshops were held in Como, Italy, on 18 and 19 April 2001, as part of - roGP 2001, the Fourth European Conference on Genetic Programming.EvoCOP focuses on applications of evolutionary algorithms and related h- ristic search methods to various combinatorial optimization problems. It also covers general methodological aspects of such algorithms like operator analyses, search dynamics, tness landscapes, and algorithmic comparisons, which are the driving force in gaining a better understanding of evolutionary search and hence support the design of e ective evolutionary algorithms for combinatorial op- mization problems of practical relevance. EvoFlight is aimed at bringing together researchers and industrial parties to discuss the use of evolutionary computation in aerospace.