An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GRASP - evolution for constraint satisfaction problems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
International Journal of Knowledge-based and Intelligent Engineering Systems
Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An enhanced statistical approach for evolutionary algorithm comparison
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Computers and Operations Research
Using Hyper-heuristics for the Dynamic Variable Ordering in Binary Constraint Satisfaction Problems
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A constraint-guided method with evolutionary algorithms for economic problems
Applied Soft Computing
Evolutionary Genetic Algorithms in a Constraint Satisfaction Problem: Puzzle Eternity II
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Managing Diversity on an AIS That Solves 3-Colouring Problems
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
IEEE Transactions on Evolutionary Computation
NAIS: a calibrated immune inspired algorithm to solve binary constraint satisfaction problems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
A conflict tabu search evolutionary algorithm for solving constraint satisfaction problems
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Graph colouring heuristics guided by higher order graph properties
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Information Sciences: an International Journal
A new distributed particle swarm optimization algorithm for constraint reasoning
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
A tabu search evolutionary algorithm for solving constraint satisfaction problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An agent model for binary constraint satisfaction problems
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Incorporating inference into evolutionary algorithms for Max-CSP
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Improving graph colouring algorithms and heuristics using a novel representation
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Managing dynamic CSPs with preferences
Applied Intelligence
ICHEA: a constraint guided search for improving evolutionary algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Solving dynamic constraint optimization problems using ICHEA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
An incremental approach to solving dynamic constraint satisfaction problems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
ICHEA for discrete constraint satisfaction problems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
Constraint handling is not straightforward in evolutionary algorithms (EAs) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade, numerous EAs for solving constraint satisfaction problems (CSP) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these EAs on a systematically generated test suite of random binary CSPs. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the evolutionary computing field.