A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Empirical Modelling of Genetic Algorithms
Evolutionary Computation
A method for parameter calibration and relevance estimation in evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adaptability of Algorithms for Real-Valued Optimization
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Model Driven Rapid Prototyping of Heuristic Optimization Algorithms
Computer Aided Systems Theory - EUROCAST 2009
A parameter-less genetic algorithm with customized crossover and mutation operators
Proceedings of the 13th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Autonomous Search
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This paper describes the optimization knowledge base (OKB), a database for storing information about algorithms and problems. The optimization knowledge base allows to save results of algorithm executions as well as problem-specific information of fitness landscape analyses. This database can be queried and gives researchers a tool for gaining a better understanding of problems and algorithms and their behavior. Therefore the OKB supports parameter tuning and keeping track of tested algorithm and parameter settings as well as their results. Furthermore, the OKB and fitness landscape analysis can be used to not only explain the behavior of algorithms but to calculate similarities between problem instances and algorithms. Based on similarities and already captured knowledge, parameter settings can be extracted that could work well for new problem instances. Additionally, the OKB can be used to publish results of experiments for a broader audience, which advocates transparency of scientific work in the area of metaheuristics.