Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
ASPARAGOS an asynchronous parallel genetic optimization strategy
Proceedings of the third international conference on Genetic algorithms
Data structures for traveling salesmen
SODA '93 Selected papers from the fourth annual ACM SIAM symposium on Discrete algorithms
Adaptive global optimization with local search
Adaptive global optimization with local search
Autocorrelation coefficient for the graph bipartitioning problem
Theoretical Computer Science
Adaptive Memory Tabu Search for Binary Quadratic Programs
Management Science
Experiments on traveling salesman heuristics
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
Memetic algorithms: a short introduction
New ideas in optimization
Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming
Journal of Heuristics
The Usefulness of Recombination
Proceedings of the Third European Conference on Advances in Artificial Life
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Clustering Gene Expression Profiles with Memetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Selected Papers from AISB Workshop on Evolutionary Computing
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
The computational complexity of N-K fitness functions
IEEE Transactions on Evolutionary Computation
Towards an analysis of dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the analysis of the (1+1) memetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Attributes of Dynamic Combinatorial Optimisation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Multiobjective Landscape Analysis and the Generalized Assignment Problem
Learning and Intelligent Optimization
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
Computing the metric dimension of graphs by genetic algorithms
Computational Optimization and Applications
Accelerating local search in a memetic algorithm for the capacitated vehicle routing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Path-guided mutation for stochastic pareto local search algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Expert Systems with Applications: An International Journal
Self-organized combinatorial optimization
Expert Systems with Applications: An International Journal
Advanced neighborhoods and problem difficulty measures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Set-based multiobjective fitness landscapes: a preliminary study
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Fitness-probability cloud and a measure of problem hardness for evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Pareto local optima of multiobjective NK-landscapes with correlated objectives
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Local search in evolutionary algorithms: the impact of the local search frequency
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Why six informants is optimal in PSO
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Toward understanding the optimization of complex systems
Artificial Intelligence Review
Information Sciences: an International Journal
Adaptation of a multiagent evolutionary algorithm to NK landscapes
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
Hi-index | 0.00 |
Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary metasearch. It is shown that the efficiency of MAs can be increased drastically with the use of domain knowledge. However, effectiveness highly depends on the structure of the problem. As is well-known, identifying this structure is made easier with the notion of fitness landscapes: the local properties of the fitness landscape strongly influence the effectiveness of the local search while the global properties strongly influence the effectiveness of the evolutionary meta-search. This paper also introduces new techniques for analyzing the fitness landscapes of combinatorial problems; these techniques focus on the investigation of random walks in the fitness landscape starting at locally optimal solutions as well as on the escape from the basins of attractions of current local optima. It is shown for NK-landscapes and landscapes of the unconstrained binary quadratic programming problem (BQP) that a random walk to another local optimum can be used to explain the efficiency of recombination in comparison to mutation. Moreover, the paper shows that other aspects like the size of the basins of attractions of local optima are important for the efficiency of MAs and a local search escape analysis is proposed. These simple analysis techniques have several advantages over previously proposed statistical measures and provide valuable insight into the behaviour of MAs on different kinds of landscapes.