Systematic and nonsystematic search strategies
Proceedings of the first international conference on Artificial intelligence planning systems
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Constraint-based reasoning
A genetic algorithm for the generalised assignment problem
Computers and Operations Research
Bounds for the frequency assignment problem
Discrete Mathematics
Using a genetic algorithm to tackle the processors configuration problem
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Guided Local Search — an Illustrative Example in Function Optimisation
BT Technology Journal
Guided Local Search for Solving SAT and Weighted MAX-SAT Problems
Journal of Automated Reasoning
Proceedings of the 6th International Conference on Genetic Algorithms
Constraint Handling in Evolutionary Search: A Case Study of the Frequency Assignment
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
GA-easy and GA-hard Constraint Satisfaction Problems
Constraint Processing, Selected Papers
Study of Genetic Search for the Frequency Assignment Problem
AE '95 Selected Papers from the European conference on Artificial Evolution
Applying a Mutation-Based Genetic Algorithm to Processor Configuration Problems
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Tackling car sequencing problems using a generic genetic algorithm
Evolutionary Computation
Systematic versus stochastic constraint satisfaction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A portable and scalable algorithm for a class of constrained combinatorial optimization problems
Computers and Operations Research
Guided local search as a network planning algorithm that incorporates uncertain traffic demands
Computer Networks: The International Journal of Computer and Telecommunications Networking
Constraint-directed search in computational finance and economics
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Inequality constraint handling in genetic algorithms using a boundary simulation method
Computers and Operations Research
Improving a local search technique for network optimization using inexact forecasts
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Load Balancing for the Dynamic Distributed Double Guided Genetic Algorithm for MAX-CSPs
International Journal of Artificial Life Research
Hi-index | 0.00 |
The Guided Genetic Algorithm (GCA) is a hybrid of Genetic Algorithm and Guided Local Search, a meta-heuristic search algorithm. As the search progresses, GGA modifies both the fitness function and fitness template of candidate solutions based on feedback from constraints. The fitness template is then used to bias crossover and mutation. The Radio Link Frequency Assignment Problem (RLFAP) is a class of problem that has practical relevance to both military and civil applications. In this paper, we show how GGA can be applied to the RLFAP. We focus on an abstraction of a real life military application that involves the assigning of frequencies to radio links. GGA was tested on a set of eleven benchmark problems provided by the French military. This set of problems has been studied intensively by a number of prominent groups in Europe. It covers a variety of needs in military applications, including the satisfaction of constraints, finding optimal solutions that satisfy all the constraints and optimization of some objective functions whenever no solution exist (“partial constraint satisfaction”). Not only do these benchmark problems vary in problem nature, they are reasonably large for military applications (up to 916 variables, and up to 5548 constraints). This makes them a serious challenge to the generality, reliability as well as efficiency of algorithms. We show in this paper that GGA is capable of producing excellent results reliably in the whole set of benchmark problems.