Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Fitness Distance Correlation and Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Critical and Empirical Study of Epistasis Measures for Predicting GA Performances: A Summary
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Information landscapes and problem hardness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A rigorous analysis of the compact genetic algorithm for linear functions
Natural Computing: an international journal
Building-block Identification by Simultaneity Matrix
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A synthesis of optimal stopping time in compact genetic algorithm based on real options approach
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms
Information Sciences: an International Journal
Chi-Square matrix: an approach for building-block identification
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
IEEE Transactions on Evolutionary Computation
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An analysis of the behavior of simplified evolutionary algorithms on trap functions
IEEE Transactions on Evolutionary Computation
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The real options technique has emerged as an evaluation tool for investment under uncertainty. It explicitly recognizes future decisions, and the exercise strategy is based on the optimal decisions in future periods. This paper employs the optimal stopping policy derived from real options approach to analyze and evaluate genetic algorithms, specifically for the new branches namely Estimation of Distribution Algorithms (EDAs). As an example, we focus on their simple class called univariate EDAs, which include the population-based incremental learning (PBIL), the univariate marginal distribution algorithm (UMDA), and the compact genetic algorithm (cGA). Although these algorithms are classified in the same class, the characteristics of their optimal stopping policy are different. These observations are useful in answering the question ''which algorithm is suitable for a particular problem''. The results from the simulations indicate that the option values can be used as a quantitative measurement for comparing algorithms.