The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Ant algorithms for discrete optimization
Artificial Life
Future Generation Computer Systems
Machine Learning
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Modeling the dynamics of ant colony optimization
Evolutionary Computation
When Model Bias Is Stronger than Selection Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Redundant representations in evolutionary computation
Evolutionary Computation
Ant Colony Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A new ant colony optimization algorithm for the multidimensional Knapsack problem
Computers and Operations Research
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Subset selection from multi-experiment data sets with application to milk fatty acid profiles
Computers and Electronics in Agriculture
Ant colony optimization for multiple knapsack problem and model bias
NAA'04 Proceedings of the Third international conference on Numerical Analysis and its Applications
Search bias in ant colony optimization: on the role of competition-balanced systems
IEEE Transactions on Evolutionary Computation
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In their quest to find a good solution to a given optimization problem, metaheuristic search algorithms intend to explore the search space in a useful and efficient manner. Starting from an initial state or solution(s), they are supposed to evolve towards high-quality solutions. For some types of genetic algorithms (GAs), it has been shown that the population of chromosomes can converge to very bad solutions, even for trivial problems. These so-called deceptive effects have been studied intensively in the field of GAs and several solutions to these problems have been proposed. Recently, similar problems have been noticed for ant colony optimization (ACO) as well. As for GAs, ACO's search can get biased towards low-quality regions in the search space, probably resulting in bad solutions. Some methods have been proposed to investigate the presence and strength of this negative bias in ACO. We present a framework that is capable of eliminating the negative bias in subset selection problems. The basic Ant System algorithm is modified to make it more robust to the presence of negative bias. A profound simulation study indicates that the modified Ant System outperforms the original version in problems that are susceptible to bias. Additionally, the proposed methodology is incorporated in the Max-Min AS and applied to a real-life subset selection problem.