Boosting the Performance of a Multiobjective Algorithm to Design RBFNNs Through Parallelization
Conference Proceeding
Guillen, A., Rojas, I., Gonzalez, J., Pomares, H., Herrera, L. J., & Paechter, B. (2007)
Boosting the Performance of a Multiobjective Algorithm to Design RBFNNs Through Parallelization. In Adaptive and Natural Computing Algorithms, 8th International Conference, ICANNGA 2007. , (85-92). https://doi.org/10.1007/978-3-540-71618-1_10
Radial Basis Function Neural Networks (RBFNNs) have been widely used to solve classification and regression tasks providing satisfactory results. The main issue when working w...
Solving optimal pump control problem using max-min ant system.
Conference Proceeding
Lopez-Ibanez, M., Prasad, T. D., & Paechter, B. (2007)
Solving optimal pump control problem using max-min ant system. In GECCO '07 Proceedings of the 9th annual conference on Genetic and evolutionary computation, (176-176). https://doi.org/10.1145/1276958.1276990
Finding feasible timetables using group-based operators.
Journal Article
Lewis, R. M. R. & Paechter, B. (2007)
Finding feasible timetables using group-based operators. IEEE Transactions on Evolutionary Computation. 11, 397-413. doi:10.1109/TEVC.2006.885162. ISSN 1089-778X
This paper describes the applicability of the so-called "grouping genetic algorithm" to a well-known version of the university course timetabling problem. We note that there a...
Metaheuristics for university course timetabling.
Book Chapter
Lewis, R. M. R., Paechter, B., & Rossi-Doria, O. (2007)
Metaheuristics for university course timetabling. In K. Dahal, K. Chen Tan, & P. Cowling (Eds.), Evolutionary Scheduling, (237-272). Berlin / Heidelberg: Springer. https://doi.org/10.1007/978-3-540-48584-1_9
In this chapter we consider the NP-complete problem of university
course timetabling. We note that it is often difficult to gain a deep understanding
of these sorts of problem...
Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation
Conference Proceeding
Guillen, A., Rojas, I., Gonzalez, J., Pomares, H., Herrera, L. J., & Paechter, B. (2006)
Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation. In AI 2006: Advances in Artificial Intelligence. , (1127-1132). https://doi.org/10.1007/11941439_135
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 i...
A tabu search evolutionary algorithm for solving constraint satisfaction problems.
Conference Proceeding
Craenen, B. G. W., & Paechter, B. (2006)
A tabu search evolutionary algorithm for solving constraint satisfaction problems. In Parallel Problem Solving from Nature - PPSN IX. , (152-161). https://doi.org/10.1007/11844297_16
The paper introduces a hybrid Tabu Search-Evolutionary Algorithm for solving the constraint satisfaction problem, called STLEA. Extensive experimental fine-tuning of parameter...
A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS
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 mu...
Application of the Grouping Genetic Algorithm to University Course Timetabling
Conference Proceeding
Lewis, R., Lewis, R. M. R., & Paechter, B. (2005)
Application of the Grouping Genetic Algorithm to University Course Timetabling. In J. Gottlieb, & G. Raidl (Eds.), Evolutionary Computation in Combinatorial Optimization, 144-153. https://doi.org/10.1007/978-3-540-31996-2_14
University Course Timetabling-Problems (UCTPs) involve the allocation of resources (such as rooms and timeslots) to all the events of a university, satisfying a set of hard-co...
Peer-to-peer networks for scalable grid landscapes in social agent simulations.
Conference Proceeding
Craenen, B. G. W., & Paechter, B. (2005)
Peer-to-peer networks for scalable grid landscapes in social agent simulations. In Proceedings of the Artificial Intelligence and Social Behaviour Convention (AISB) 2005, 64-71
Recently, peer-to-peer networks have been proposed as the underlying architecture of large scale distributed social agent simulations. A number of problems arise when grid lan...
Fine-tuning a Genetic Algorithm for the General Timetable Problem
Conference Proceeding
Luchian, H., Ungureanasu, C., Paechter, B., & Petriuc, M. (2005)
Fine-tuning a Genetic Algorithm for the General Timetable Problem. In The Practice and Theory of Automated Timetabling I. , (435-442