J. Fieldsend, R. Everson, and S. Singh, "Using Unconstrained Elite Archives for Multi-Objective Optimisation," IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 305--323, 2003. Home/Search Document Details and Download
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Dominance-Based Multi-Objective Simulated Annealing - Smith, Everson, Fieldsend, .. Self-citation (Fieldsend Everson) (Correct)
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J. Fieldsend, R. Everson, and S. Singh, "Using Unconstrained Elite Archives for Multi-Objective Optimisation," IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 305--323, 2003.
Multi-Objective Particle Swarm Optimisation Methods. - Jonathan Fieldsend St Self-citation (Fieldsend) (Correct)
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J.E. Fieldsend, R.M. Everson, and S. Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation, 7(3):305--323, 2003.
Multi-Class ROC Analysis from a . . . - Everson (2005) Self-citation (Fieldsend Everson) (Correct)
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J.E. Fieldsend, R.M. Everson, and S. Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation, 7(3):305--323, 2003.
A Short Note On The Ecient Random Sampling Of The.. - Fieldsend (2005) Self-citation (Fieldsend) (Correct)
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J.E. Fieldsend, R.M. Everson, and S. Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation, 7(3):305--323, 2003.
Formulation and comparison of multi-class ROC surfaces - Fieldsend, Everson (2005) Self-citation (Fieldsend Everson) (Correct)
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Fieldsend, J., Everson, R., & Singh, S. (2003). Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Trans. Evol. Comp., 7, 305-- 323.
Formulation and comparison of multi-class ROC surfaces - Fieldsend, Everson (2005) Self-citation (Fieldsend Everson) (Correct)
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Fieldsend, J., Everson, R., & Singh, S. (2003). Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Trans. Evol. Comp., 7, 305-- 323.
Pareto Evolutionary Neural Networks - Jonathan Fieldsend Member Self-citation (Fieldsend Singh) (Correct)
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J. Fieldsend, R. Everson, and S. Singh, "Using Unconstrained Elite Archives for Multi-Objective Optimisation," IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 305--323, 2003.
Dominance Measures for Multi-Objective Simulated Annealing - Smith, Everson, Fieldsend (2004) Self-citation (Fieldsend Everson) (Correct)
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J.E. Fieldsend, R.M. Everson, and S. Singh, "Using Unconstrained Elite Archives for Multi-Objective Optimisation," IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 305--323, 2003.
Cardinality Constrained Portfolio Optimisation - Fieldsend, Matatko, Peng Self-citation (Fieldsend) (Correct)
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Fieldsend, J., Everson, R., Singh, S.: Using Unconstrained Elite Archives for MultiObjective Optimisation. IEEE Trans. on Evol. Comp. 7 (2003) 305--323
ROC Optimisation of Safety Related Systems - Jonathan Fieldsend Richard (2004) Self-citation (Fieldsend Everson) (Correct)
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J.E. Fieldsend, R.M. Everson, and S. Singh, `Using Unconstrained Elite Archives for Multi-Objective Optimisation', IEEE Transactions on Evolutionary Computation, 7(3), 305--323, (2003).
Dominance Measures for Multi-Objective Simulated Annealing - Smith, Everson, Fieldsend. (2004) Self-citation (Fieldsend Everson) (Correct)
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J.E. Fieldsend, R.M. Everson, and S. Singh, "Using Unconstrained Elite Archives for Multi-Objective Optimisation," IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 305--323, 2003.
Pareto Evolutionary Neural Networks - Fieldsend, Singh (2003) Self-citation (Fieldsend Singh) (Correct)
....a well defined approach to implementing multiple objective training within NNs is needed. Through the use of a multi objective evolutionary algorithms (MOEAs) it is possible to find an estimated Pareto set of the combination of parameters to multiple objective clean function modelling problems [9, 11, 17, 19, 49, 58]. A Pareto set of solutions is defined such that in a set of parameter combinations , no single parameter combination F i is better or equivalent on all other objective measures, than any other set member Fj. That is no parameter combination dominates any other parameter combinations in the set. ....
....maintained. Most recent work in the MOEA domain has been concerned with how the search population of the EC process and F should interact, and how to evolve the search as time progresses. Recent work has also investigated efficient approaches to the storing and maintenance of F as its size grows [14, 17, 41]. Here a framework for general multi objective evolutionary neural network (MOENN) training is outlined in Figure 2, which can be viewed as a synthesis of work from the uni objective ENN literature and the MOEA literature. In this new framework a set of estimated Pareto optimal ENNs is maintained ....
[Article contains additional citation context not shown here]
J.E. Fieldsend, R.M. Everson, and S. Singh. Using Unconstrained Elite Archives for Multi-Objective Optimi- sation. IEEE Transactions on Evolutionary Computation (forthcoming), 2003.
Multi-Objective Optimisation for Information Access Tasks - Fisher, Fieldsend, Everson (2003) Self-citation (Fieldsend Everson) (Correct)
.... The MOEA used in this study is a based on a simple (1 1) ES, similar to that introduced in [13] This version however maintains an unconstrained archive of the Pareto optimal solutions found so far in the search process (instead of limiting its size) and uses the new data structures introduced in [6]. In outline, the procedure for locating the Pareto front, operates by maintaining an archive, # of mutually nondominating solutions, At each stage of the algorithm some solutions in # are copied and perturbed. Those perturbed solutions that are dominated by members of # are discarded, ....
....combinations. 3: Insert those individuals in , that are not dominated into , # # . 4: Remove from # any individuals that are dominated by the new entrants. 4: Copy a decision vector, from # , Selected in this study using the partitioned quasi random selection method [6]) 5: Perturb the decision variables of , if ##### ##### # # else ##### ##### # # ######### ##### ensuring that the new parameters lie in valid ranges. Copy # times with different thresholds in its last element (generating set ) # ### 6: If # , goto 7, else goto 2. 7: ....
[Article contains additional citation context not shown here]
J.E. Fieldsend, R.M. Everson, and S. Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation, page Forthcoming, 2003.
A Multi-Objective Algorithm based upon Particle Swarm.. - Fieldsend, Singh (2002) (2 citations) Self-citation (Fieldsend Singh) (Correct)
....of PSO to the multi objective domain is a natural progression. In this paper we argue that current attempts at multi objective PSO do not fully transfer the PSO heuristic to the multiobjective domain. We therefore introduce a new method that utilises the recent dominated trees data structure [3, 4] to enable the selection of an appropriate Pareto archive member to act as the global best for any given particle, and also maintains a local set of best solutions for each swarm member. We then demonstrate that this approach is significantly better than the method used in It] and an ....
....significantly increases performance. The paper takes the following structure: in Section 2 Pareto optimality is reviewed; in Section 3 PSO is briefly described, as are two current applications in the literature of multiobjective PSO. In Section 4 one of the data structures introduced in [4], dominated trees, is described, in preparation for the key role it plays in the multi objective PSO method introduced in section 5. A set of experiments to quantify the performance of this new multi objective PSO algorithm, in comparison to an ES method and an existing multi objective PSO are ....
[Article contains additional citation context not shown here]
J.E. Fieldsend, R.M. Everson, and S.Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation (Submitted), 2001.
On the Selection of Gbest, Lbest and Pbest Individuals, the.. - Fieldsend, Singh (2002) Self-citation (Fieldsend Singh) (Correct)
....the true Pareto front. The goal, therefore, of multi objective algorithms (MOAs) is to locate the Pareto front of these non dominated solutions. Multi Objective Evolutionary Algorithms (MOEAs) are a popular approach to confronting these types of problem by using evolutionary search techniques [1, 4, 7, 5, 9, 8, 10, 12, 13, 17, 16, 19, 30, 20, 22, 24, 26, 27, 29, 31, 28]. The use of Evolutionary Algorithms (EAs) as a tool of preference is due to such problems being typically complex, with both a large number of parameters to be adjusted, and several objectives to be optimised. EAs, which can maintain a population of solutions, are in addition able to explore ....
....In this paper we compare a number of different selection methods of appropriate gbest lbest and pbest for each swarm member. The comparative selection methods include those used previously, 2, 6] and completely new selection methods, an number of which are based upon a recent data structure [5]. A brief critique of other methods for selection in MOPSO [14, 23] is also provided. The new MOPSO models are compared both with each other and an Evolutionary Strategy (ES) derived from the Unified Model of Laumanns et al. 19] which is based upon an existing MOEA [17] Furthermore we ....
[Article contains additional citation context not shown here]
J.E. Fieldsend, R.M. Everson, and S.Singh. Using Unconstrained Elite Archives for Multi-Objective Optimisation. IEEE Transactions on Evolutionary Computation (Submitted), 2001.
Vector Evaluated Differential Evolution for.. - Parsopoulos.. (2004) (Correct)
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J. E. Fieldsend, R. M. Everson, and S. Singh. Using unconstrained elite archives for multiobjective optimization. IEEE Trans. Evol. Comp., 7(3):305--323, 2003.
Bounded Archiving using the Lebesgue Measure - Knowles, Corne, Fleischer (2003) (1 citation) (Correct)
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Jonathan E. Fieldsend, Richard M. Everson, and Sameer Singh. Using unconstrained elite archives for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 7(3):305--323, June 2003.
Vector Evaluated Differential Evolution for.. - Parsopoulos.. (2004) (Correct)
No context found.
J. E. Fieldsend, R. M. Everson, and S. Singh. Using unconstrained elite archives for multiobjective optimization. IEEE Trans. Evol. Comp., 7(3):305--323, 2003.
Multiobjective Optimization Using Parallel Vector.. - Parsopoulos.. (2004) (Correct)
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J. E. Fieldsend, R. M. Everson, and S. Singh. Using unconstrained elite archives for multiobjective optimization. IEEE Trans. Evol. Comp., 7(3):305--323, 2003.
Multiobjective Optimization Using Parallel Vector.. - Parsopoulos.. (2004) (Correct)
No context found.
J. E. Fieldsend, R. M. Everson, and S. Singh. Using unconstrained elite archives for multiobjective optimization. IEEE Trans. Evol. Comp., 7(3):305--323, 2003.
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