Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Parallel biased search for combinatorial optimization: genetic algorithms and TABU
Microprocessors & Microsystems
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Parallel Genetic Heuristic for the Quadratic Assignment Problem
Proceedings of the 3rd International Conference on Genetic Algorithms
On the State of Evolutionary Computation
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
On Multi-Dimensional Encoding/Crossover
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
Proceedings of the 6th International Conference on Genetic Algorithms
Applying Genetic Algorithms to Outlier Detection
Proceedings of the 6th International Conference on Genetic Algorithms
Design of Statistical Quality Control Procedures Using Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Population-Oriented Simulated Annealing: A Genetic/Thermodynamic Hybrid Approach to Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Boltzmann-, Darwin-, and Haeckel-Strategies in Optimization Problems
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Evolving High-Posterior Self-Organizing Maps
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Matrix-based genetic algorithm for computing the minimum volume ellipsoid
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
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Genetic algorithms (GAs) are adaptive search techniques designed to find near-optimal solutions of large scale optimization problems with multiple local maxima. Standard versions of the GA are defined for objective functions which depend on a vector of binary variables. The problem of finding the maximum a posteriori (MAP) estimate of a binary image in Bayesian image analysis appears to be well suited to a GA as images have a natural binary representation and the posterior image probability is a multi-modal objective function. We use the numerical optimization problem posed in MAP image estimation as a test-bed on which to compare GAs with simulated annealing (SA), another all-purpose global optimization method. Our conclusions are that the GAs we have applied perform poorly, even after adaptation to this problem. This is somewhat unexpected, given the widespread claims of GAs‘ effectiveness, but it is in keeping with work by Jennison and Sheehan (1995) which suggests that GAs are not adept at handling problems involving a great many variables of roughly equal influence.We reach more positive conclusions concerning the use of the GA‘s crossover operation in recombining near-optimal solutions obtained by other methods. We propose a hybrid algorithm in which crossover is used to combine subsections of image reconstructions obtained using SA and we show that this algorithm is more effective and efficient than SA or a GA individually.