Recent Advances in Evolutionary Computation for Combinatorial Optimization Export

@book{citeulike:3757181, abstract = {Combinatorial optimisation is a ubiquitous discipline. Its applications range from telecommunications to logistics, from manufacturing to personnel scheduling, from bioinformatics to management, and a long et cetera. Combinatorial opti- misation problems (COPs) are characterised by the need of finding an optimal or quasi-optimal assignment of values to a number of discrete variables, with respect to a certain objective function or to a collection of objective functions. The economical, technological and societal impact of this class of problems is out of question, and has driven the on-going quest for effective solving strategies. Initial approaches for tackling COPs were based on exact methods, but the intrinsic complexity of most problems in the area make such methods unafford- able for realistic problem instances. Approximate methods have been defined as well as, but in general these are far from practical too, and do not provide a systematic line of attack to deal with COPs. Parameterised complexity algo- rithms allow efficiently solving certain COPs for which the intrinsic hardness is isolated within an internal structural parameter, whose value can be kept small. For the remaining problems (most COPs actually), practical solving requires the use of metaheuristic approaches such as, evolutionary algorithms, swarm intelligence and local search techniques. Dating back to the last decades of the twentieth century, these methods trade completeness for pragmatic effectiveness, thereby providing probably optimal or quasi-optimal solutions to a plethora of hard COPs. The application of metaheuristics to COPs is an active field in which new the- oretical developments, new algorithmic models, and new application areas are continuously emerging. In this sense, this volume presents recent advances in the area of metaheuristic combinatorial optimisation. The most popular metaheuris- tic family is evolutionary computation (EC), and as such an important part of the volume deals with EC approaches. However, contributions in this volume are not restricted to EC, and comprise metaheuristics as a whole.}, citeulike-article-id = {3757181}, editor = {Cotta, Carlos and van Hemert, Jano}, keywords = {combinatorial, engineering, evolutionary-computation, metaheuristic, nk-landscapes, stochastic-local-search}, posted-at = {2008-12-08 15:57:27}, priority = {4}, publisher = {Springer}, series = {Studies in Computational Intelligence}, title = {Recent Advances in Evolutionary Computation for Combinatorial Optimization}, volume = {153}, year = {2008} }

TY - BOOK ID - citeulike:3757181 L3 - citeulike-article-id:3757181 N2 - Combinatorial optimisation is a ubiquitous discipline. Its applications range from telecommunications to logistics, from manufacturing to personnel scheduling, from bioinformatics to management, and a long et cetera. Combinatorial opti- misation problems (COPs) are characterised by the need of finding an optimal or quasi-optimal assignment of values to a number of discrete variables, with respect to a certain objective function or to a collection of objective functions. The economical, technological and societal impact of this class of problems is out of question, and has driven the on-going quest for effective solving strategies. Initial approaches for tackling COPs were based on exact methods, but the intrinsic complexity of most problems in the area make such methods unafford- able for realistic problem instances. Approximate methods have been defined as well as, but in general these are far from practical too, and do not provide a systematic line of attack to deal with COPs. Parameterised complexity algo- rithms allow efficiently solving certain COPs for which the intrinsic hardness is isolated within an internal structural parameter, whose value can be kept small. For the remaining problems (most COPs actually), practical solving requires the use of metaheuristic approaches such as, evolutionary algorithms, swarm intelligence and local search techniques. Dating back to the last decades of the twentieth century, these methods trade completeness for pragmatic effectiveness, thereby providing probably optimal or quasi-optimal solutions to a plethora of hard COPs. The application of metaheuristics to COPs is an active field in which new the- oretical developments, new algorithmic models, and new application areas are continuously emerging. In this sense, this volume presents recent advances in the area of metaheuristic combinatorial optimisation. The most popular metaheuris- tic family is evolutionary computation (EC), and as such an important part of the volume deals with EC approaches. However, contributions in this volume are not restricted to EC, and comprise metaheuristics as a whole. SE - Studies in Computational Intelligence TI - Recent Advances in Evolutionary Computation for Combinatorial Optimization PB - Springer VL - 153 KW - combinatorial KW - engineering KW - evolutionary-computation KW - metaheuristic KW - nk-landscapes KW - stochastic-local-search A2 - Cotta, Carlos A2 - van Hemert, Jano PY - 2008/// ER -