Stable adaptive systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Not all linear functions are equally difficult for the compact genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The equation for response to selection and its use for prediction
Evolutionary Computation
A study on the global convergence time complexity of estimation of distribution algorithms
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A family of compact genetic algorithms for intrinsic evolvable hardware
IEEE Transactions on Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Cooperative coevolution and univariate estimation of distribution algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Disturbed Exploitation compact Differential Evolution for limited memory optimization problems
Information Sciences: an International Journal
On the optimal convergence probability of univariate estimation of distribution algorithms
Evolutionary Computation
Compact Particle Swarm Optimization
Information Sciences: an International Journal
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The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized.