Cooperative ant colonies for solving the maximum weighted satisfiability problem

Authors:
Habiba Drias;Amine Taibi;Sofiane Zekour
Affiliations:
USTHB, Computer Science Department, Laboratory of Research in Artificial Intelligence, Algiers, Algeria;USTHB, Computer Science Department, Laboratory of Research in Artificial Intelligence, Algiers, Algeria;USTHB, Computer Science Department, Laboratory of Research in Artificial Intelligence, Algiers, Algeria
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 5
Cited 1

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

Artificial Intelligence Review
A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA)

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

ACO approach with learning for preemptive scheduling of real-time tasks

International Journal of Bio-Inspired Computation

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Abstract

This paper presents a new generic Evolutionary Algorithm (EA) for retarding the unwanted effects of premature convergence. This is accomplished by a combination of interacting methods. To be intent on this a new selection scheme is introduced, which is designed to maintain the genetic diversity within the population by advantageous self-adaptive steering of selection pressure. Additionally this new selection model enables a quite intuitive condition to detect premature convergence. Based upon this newly postulated basic principle the new selection mechanism is combined with the already proposed Segregative Genetic Algorithm (SEGA) [3], an advanced Genetic Algorithm (GA) that introduces parallelism mainly to improve global solution quality. As a whole, a new generic evolutionary algorithm (SASEGASA) is introduced. The performance of the algorithm is evaluated on a set of characteristic benchmark problems. Computational results show that the new method is capable of producing highest quality solutions without any problem-specific additions.