Abstract
Mate-selection is the problem of deciding which animal should be culled and which should be mated in a breeding program. In addition, if the animal is to be mated, which animal from the other sex should it be mated with? With a linear objective function, integer linear programming was successfully used to solve the problem. Also, when the relation between the animals’ traits is additive, a strategy based on the classical index theory for selection with random allocation, results in the optimal solution. Today, non-linear and non-additive objectives are introduced and the problem is becoming increasingly complex. In this paper, we formulate the problem as a multi-stage quadratic transportation model. We then introduce a new version of Tabu Search that we call Markov Chain Tabu Search (MCTS) for solving the mate-selection problem. We then compare MCTS against five conventional heuristic techniques; these are, random search, hill climbing, simulated annealing, sequential genetic algorithms, and simultaneous genetic algorithms. MCTS is found statistically significantly better than the other heuristics for handling this large scale mate-selection problem.
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Abbass, H.A., Towsey, M., Kozan, E., Van der Werf, J. (2002). A Markov Chain Tabu Search Approach to the Evolutionary Allocation Problem. In: Kozan, E., Ohuchi, A. (eds) Operations Research/Management Science at Work. International Series in Operations Research & Management Science, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0819-9_5
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DOI: https://doi.org/10.1007/978-1-4615-0819-9_5
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