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- Joshua D. Knowles, David W. Corne
- Evolutionary Computation
- 2000

We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1 + 1) evolution strategy employing… (More)

Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we introduce a simpler evolution scheme for multiobjective problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible… (More)

- David W. Corne, Nick R. Jerram, Joshua D. Knowles, Martin J, Oates
- 2001

We describe a new selection technique for evolutionary multiobjective optimization algorithms in which the unit of selection is a hyperbox in objective space. In this technique , instead of assigning a selective tness to an individual, selective tness is assigned to the hyperboxes in objective space which are currently occupied by at least one individual in… (More)

- Julia Handl, Joshua D. Knowles
- IEEE Trans. Evolutionary Computation
- 2007

- David W. Corne, Joshua D. Knowles, Martin J. Oates
- PPSN
- 2000

We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA's selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use… (More)

- Krzysztof Socha, Joshua D. Knowles, Michael Sampels
- Ant Algorithms
- 2002

We consider a simplification of a typical university course timetabling problem involving three types of hard and three types of soft constraints. A MAX -MIN Ant System, which makes use of a separate local search routine, is proposed for tackling this problem. We devise an appropriate construction graph and pheromone matrix representation after considering… (More)

- Julia Handl, Joshua D. Knowles, Douglas B. Kell
- Bioinformatics
- 2005

MOTIVATION
The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the… (More)

Evolutionary multi objective optimisation EMO now boasts a proliferation of algorithms and bench mark problems We need principled ways to compare the performance of di erent EMO algorithms but this is com plicated by the fact that the result of an EMO run is not a single scalar value but a collection of vectors forming a non dominated set Various metrics… (More)

A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new algorithm is carried out by testing it on a set of multiobjective 0/1 knapsack… (More)