TY - Generic T1 - Evolutionary Computation in Combinatorial Optimization, 8th European Conference T2 - Lecture Notes in Computer Science Y1 - 2008 A1 - van Hemert, Jano A1 - Cotta, Carlos ED - van Hemert, Jano ED - Cotta, Carlos KW - evolutionary computation AB - Metaheuristics have shown to be effective for difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization and estimation of distribution algorithms. Problems solved successfully include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the travelling salesman problem, packing and cutting, satisfiability and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It is the first event specifically dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop, EvoCOP became a conference in 2004. The events gave researchers an excellent opportunity to present their latest research and to discuss current developments and applications. Following the general trend of hybrid metaheuristics and diminishing boundaries between the different classes of metaheuristics, EvoCOP has broadened its scope over the last years and invited submissions on any kind of metaheuristic for combinatorial optimization. JF - Lecture Notes in Computer Science PB - Springer VL - LNCS 4972 ER - TY - CONF T1 - Graph Colouring Heuristics Guided by Higher Order Graph Properties T2 - Lecture Notes in Computer Science Y1 - 2008 A1 - Juhos, Istv\'{a}n A1 - van Hemert, Jano ED - van Hemert, Jano ED - Cotta, Carlos KW - evolutionary computation KW - graph colouring AB - Graph vertex colouring can be defined in such a way where colour assignments are substituted by vertex contractions. We present various hyper-graph representations for the graph colouring problem all based on the approach where vertices are merged into groups. In this paper, we show this provides a uniform and compact way to define algorithms, both of a complete or a heuristic nature. Moreover, the representation provides information useful to guide algorithms during their search. In this paper we focus on the quality of solutions obtained by graph colouring heuristics that make use of higher order properties derived during the search. An evolutionary algorithm is used to search permutations of possible merge orderings. JF - Lecture Notes in Computer Science PB - Springer VL - 4972 ER - TY - BOOK T1 - Recent Advances in Evolutionary Computation for Combinatorial Optimization T2 - Studies in Computational Intelligence Y1 - 2008 A1 - Cotta, Carlos A1 - van Hemert, Jano AB - Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems. The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective. JF - Studies in Computational Intelligence PB - Springer VL - 153 SN - 978-3-540-70806-3 UR - http://www.springer.com/engineering/book/978-3-540-70806-3 ER - TY - Generic T1 - Evolutionary Computation in Combinatorial Optimization, 7th European Conference T2 - Lecture Notes in Computer Science Y1 - 2007 A1 - Cotta, Carlos A1 - van Hemert, Jano ED - Carlos Cotta ED - van Hemert, Jano KW - evolutionary computation AB - Metaheuristics have often been shown to be effective for difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization. Successfully solved problems include scheduling, timetabling, network design, transportation and distribution, vehicle routing, the traveling salesman problem, satisfiability, packing and cutting, and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It was the first event specifically dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. Originally held as a workshop, EvoCOP became a conference in 2004. The events gave researchers an excellent opportunity to present their latest research and to discuss current developments and applications as well as providing for improved interaction between members of this scientific community. Following the general trend of hybrid metaheuristics and diminishing boundaries between the different classes of metaheuristics, EvoCOP has broadened its scope over the last years and invited submissions on any kind of metaheuristic for combinatorial optimization. JF - Lecture Notes in Computer Science PB - Springer VL - LNCS 4446 UR - http://springerlink.metapress.com/content/105633/ ER -