Properties of fitness functions and search landscapes
Theoretical aspects of evolutionary computing
On classifications of fitness functions
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
On the Feasibility Problem of Penalty-Based Evolutionary Algorithms for Knapsack Problems
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Information landscapes and the analysis of search algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Information landscapes and problem hardness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
A study of NK landscapes' basins and local optima networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Characterizing warfare in red teaming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Characterizing Game Dynamics in Two-Player Strategy Games Using Network Motifs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multidimensional Knapsack Problem: A Fitness Landscape Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constrained layout optimization in satellite cabin using a multiagent genetic algorithm
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
On the taxonomy of optimization problems under estimation of distribution algorithms
Evolutionary Computation
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One of the major challenges in the field of evolutionary algorithms (EAs) is to characterise which kinds of problems are easy and which are not. Researchers have been attracted to predict the behaviour of EAs in different domains. We introduce fitness landscape networks (FLNs) that are formed using operators satisfying specific conditions and define a new predictive measure that we call motif difficulty (MD) for comparison-based EAs. Because it is impractical to exhaustively search the whole network, we propose a sampling technique for calculating an approximate MD measure. Extensive experiments on binary search spaces are conducted to show both the advantages and limitations of MD. Multidimensional knapsack problems (MKPs) are also used to validate the performance of approximate MD on FLNs with different topologies. The effect of two representations, namely binary and permutation, on the difficulty of MKPs is analysed.