A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Parallel recombinative simulated annealing: a genetic algorithm
Parallel Computing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Journal of Global Optimization
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Efficient bayesian network inference: genetic algorithms, stochastic local search, and abstraction
Efficient bayesian network inference: genetic algorithms, stochastic local search, and abstraction
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
The crowding approach to niching in genetic algorithms
Evolutionary Computation
Advances in Differential Evolution
Advances in Differential Evolution
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
To explore or to exploit: An entropy-driven approach for evolutionary algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
Real-parameter genetic algorithms for finding multiple optimal solutions in multi-modal optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Generalized crowding for genetic algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
On Evolutionary Exploration and Exploitation
Fundamenta Informaticae
Theoretical Foundations of Order-Based Genetic Algorithms
Fundamenta Informaticae
A novel mating approach for genetic algorithms
Evolutionary Computation
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The genetic algorithm technique known as crowding preserves population diversity by pairing each offspring with a similar individual in the current population (pairing phase) and deciding which of the two will survive (replacement phase). The replacement phase of crowding is usually carried out through deterministic or probabilistic crowding, which have the limitations that they apply the same selective pressure regardless of the problem being solved and the stage of genetic algorithm search. The recently developed generalized crowding approach introduces a scaling factor in the replacement phase, thus generalizing and potentially overcoming the limitations of both deterministic and probabilistic crowding. A key problem not previously addressed, however, is how the scaling factor should be adapted during the search process in order to effectively obtain optimal or near-optimal solutions. The present work investigates this problem by developing and evaluating two methods for adapting, during search, the scaling factor. We call these two methods diversity-adaptive and self-adaptive generalized crowding respectively. Whereas the former method adapts the scaling factor according to the population's diversity, the latter method includes the scaling factor in the chromosome for self-adaptation. Our experiments with real function optimization, Bayesian network inference, and the Traveling Salesman Problem show that both diversity-adaptive and self-adaptive generalized crowding are consistent techniques that produce strong results, often outperforming traditional generalized crowding.