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Outputs (93)

Fine-tuning a Genetic Algorithm for the General Timetable Problem (2005)
Presentation / Conference Contribution
Luchian, H., Ungureanasu, C., Paechter, B., & Petriuc, M. (1995, August). Fine-tuning a Genetic Algorithm for the General Timetable Problem. Presented at 1st International Conference on the Practice and Theory of Automated Timetabling (PATAT 1995), Edinburgh

Peer-to-peer networks for scalable grid landscapes in social agent simulations. (2005)
Presentation / Conference Contribution
Craenen, B. G. W., & Paechter, B. (2005, April). Peer-to-peer networks for scalable grid landscapes in social agent simulations. Presented at AISB’05 Convention Social Intelligence and Interaction in Animals, Robots and Agents, Hatfield, UK

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 landscapes are used to represent the landscape in these simulations, primarily... Read More about Peer-to-peer networks for scalable grid landscapes in social agent simulations..

Parallelization of population-based multi-objective meta-heuristics: An empirical study (2005)
Journal Article
Baños, R., Gil, C., Paechter, B., & Ortega, J. (2006). Parallelization of population-based multi-objective meta-heuristics: An empirical study. Applied Mathematical Modelling, 30(7), 578-592. https://doi.org/10.1016/j.apm.2005.05.021

In single-objective optimization it is possible to find a global optimum, while in the multi-objective case no optimal solution is clearly defined, but several that simultaneously optimize all the objectives. However, the majority of this kind of pro... Read More about Parallelization of population-based multi-objective meta-heuristics: An empirical study.

An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case. (2005)
Presentation / Conference Contribution
Lewis, R., & Paechter, B. (2005, September). An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case. Presented at 2005 IEEE Congress on Evolutionary Computation, Edinburgh, Scotland

A grouping genetic algorithm (GGA) for the university course timetabling problem is outlined. We propose six different fitness functions, all sharing the same common goal, and look at the effects that these can have on the algorithm with respect to b... Read More about An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case..

Maintaining Connectivity in a Scalable and Robust Distributed Environment (2005)
Presentation / Conference Contribution
Jelasity, M., Preuss, M., van Steen, M., & Paechter, B. (2002, May). Maintaining Connectivity in a Scalable and Robust Distributed Environment. Presented at 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02), Berlin, Germany

This paper describes a novel peer-to-peer (P2P) environment for running distributed Java applications on the Internet. The possible application areas include simple load balancing, parallel evolutionary computation, agent-based simulation and artific... Read More about Maintaining Connectivity in a Scalable and Robust Distributed Environment.

Multi-Objective Optimisation of the Pump Scheduling Problem using SPEA2. (2005)
Presentation / Conference Contribution
Lopez-Ibanez, M., Devi Prasad, T., & Paechter, B. (2005, September). Multi-Objective Optimisation of the Pump Scheduling Problem using SPEA2. Presented at IEEE Congress on Evolutionary Computation

Significant operational cost and energy savings can be achieved by optimising the schedules of pumps, which pump water from source reservoirs to storage tanks, in water distribution networks. Despite the fact that pump scheduling problem involves sev... Read More about Multi-Objective Optimisation of the Pump Scheduling Problem using SPEA2..

New crossover operators for timetabling with evolutionary algorithms. (2004)
Presentation / Conference Contribution
Lewis, R. M. R., & Paechter, B. (2004, December). New crossover operators for timetabling with evolutionary algorithms

When using an evolutionary algorithm (EA) to optimise a population of feasible course timetables, it is important that the mutation and crossover operators are designed in such a way so that they don?t produce unfeasible or illegal offspring. In this... Read More about New crossover operators for timetabling with evolutionary algorithms..

PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation (2004)
Journal Article
de Toro Negro, F., Ortega, J., Ros, E., Mota, S., Paechter, B., & Martin, J. M. (2004). PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation. Parallel Computing, 30(5-6), 721-739. https://doi.org/10.1016/j.parco.2003.12.012

This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single fron... Read More about PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation.