Simple genetic algorithm with local tuning: efficient global optimizing technique
Journal of Optimization Theory and Applications
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
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
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Extending the GA-EDA hybrid algorithm to study diversification and intensification in GAs and EDAs
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
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This paper introduces a new hybrid Genetic Algorithm (GA) crossover approach, Targeted EDA (TEDA), that combines a targeted intervention principle with Estimation of Distribution Algorithms (EDA) to solve optimal control problems. The approach is suited to tasks where the number of interventions used is an important part of solution fitness and includes problems such as cancer chemotherapy scheduling. Fitness Directed Crossover (FDC) is a modified GA crossover method that actively drives the number of selected control interventions towards those of a fitter individual. EDA are able to find fit solutions by discovering patterns within a population of selected individuals. TEDA uses FDC to select a suitable number of interventions to use while using an EDA based approach to select which interventions to set. Results suggest that by combining the two approaches, TEDA is able to outperform both EDA and FDC on a sample optimal control problem.