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  - 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  -