Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Population-based algorithm portfolios for numerical optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Crossover can be constructive when computing unique input–output sequences
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Optimization and Learning
A large population size can be unhelpful in evolutionary algorithms
Theoretical Computer Science
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Measuring Generalization Performance in Coevolutionary Learning
IEEE Transactions on Evolutionary Computation
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In this third article in the ACM Ubiquity symposium on evolutionary computation Xin Yao provides a deeper understanding of evolutionary algorithms in the context of classical computational paradigms. This article discusses some of the most important issues in evolutionary computation. Three major areas are identified. The first is the theoretical foundation of evolutionary computation, especially the computational time complexity analysis. The second is on algorithm design, especially on hybridization, memetic algorithms, algorithm portfolios and ensembles of algorithms. The third is co-evolution, which seems to be under studied in both theory and practice. The primary aim of this article is to stimulate further discussions, rather than to offer any solutions.