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Prof Ben Paechter's Outputs (3)

Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation (2006)
Presentation / Conference Contribution
Guillen, A., Rojas, I., Gonzalez, J., Pomares, H., Herrera, L. J., & Paechter, B. (2006, December). Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation. Presented at 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia

Nature shows many examples where the specialisation of elements aimed to solve different problems is successful. There are explorer ants, worker bees, etc., where a group of individuals is assigned a specific task. This paper will extrapolate this ph... Read More about Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation.

A tabu search evolutionary algorithm for solving constraint satisfaction problems. (2006)
Presentation / Conference Contribution
Craenen, B. G. W., & Paechter, B. (2006, September). A tabu search evolutionary algorithm for solving constraint satisfaction problems. Presented at Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, Reykjavik, Iceland

The paper introduces a hybrid Tabu Search-Evolutionary Algorithm for solving the constraint satisfaction problem, called STLEA. Extensive experimental fine-tuning of parameters of the algorithm was performed to optimise the performance of the algorit... Read More about A tabu search evolutionary algorithm for solving constraint satisfaction problems..

A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS (2006)
Journal Article
Baños, R., Gil, C., Paechter, B., & Ortega, J. (2007). A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS. Journal of Mathematical Modelling and Algorithms, 6(2), 213-230. https://doi.org/10.1007/s10852-006-9041-6

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set... Read More about A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS.