Deb, K. and Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization--Lecture Notes in Computer Science 1993, Zurich, Switzerland (2001) 67--81  Home/Search   Document Details and Download   Summary   Related Articles   Check

....experimental results of PICPA on some famous test problems. Given the deterministic nature of PICPA, the quality of solutions of PICPA can be directly assessed with respect to the nal bounds found. To show its practical performance however, we contrast the results of PICPA with those of NSGA IIc [7] . Notice that the version of the NSGA IIc algorithm used here gives better results than those given in [7] For these test experiments, the following parameter settings are used: for NSGA IIc, we used the settings given in [8] i.e. simulated binary crossover [6] with n c = 20 and the ....

....of solutions of PICPA can be directly assessed with respect to the nal bounds found. To show its practical performance however, we contrast the results of PICPA with those of NSGA IIc [7] Notice that the version of the NSGA IIc algorithm used here gives better results than those given in [7]. For these test experiments, the following parameter settings are used: for NSGA IIc, we used the settings given in [8] i.e. simulated binary crossover [6] with n c = 20 and the polynomial mutation operator with nm = 20. A crossover probability of 0.9 and a mutation probability of 0.15 are ....

K. Deb and T. Goel. Controlled elitist non-dominated sorting genetic algorithms for better convergence. In Proceedings of Evolutionary Multi-Criterion Optimization, pages 6781, 2001.

....being optimized. On a bidimensional search space (two objectives) such set is known as the Pareto front. Deb wrote a comprehensive book on the subject [2] presenting many algorithms and techniques to evaluate their performance. For our research we have chosen the Controlled Elitist NSGA [3], based on previous experiments with many algorithms on a set of standard problems [10] Based on the well known NSGA and NSGA II algorithms, the controlled elitist NSGA features more diversity on the population than other algorithms, which is desirable to allow the algorithm to explore better the ....

K. Deb and T. Goel. Controlled elitist non-dominated sorting genetic algorithms for better convergence. In E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne, editors, Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001.

....results of PICP.4 on some famous test problems. Given the deterministic nature of PICP.4, the quality of solutions of PICP.4 can be directly assessed with respect to the final bounds found. To show its practical performance however, we contrast the results of PICP.4 with those of NSG.4 IIc [7]. Notice that the version of the N .4 IIc algorithm used here gives better results than those given in [7] For these test experiments, the following parameter settings are used: for N .4 IIc, we used the settings given in [8] i.e. simulated binary crossover [6] with nc = 20 and the ....

....of solutions of PICP.4 can be directly assessed with respect to the final bounds found. To show its practical performance however, we contrast the results of PICP.4 with those of NSG.4 IIc [7] Notice that the version of the N . 4 IIc algorithm used here gives better results than those given in [7]. For these test experiments, the following parameter settings are used: for N .4 IIc, we used the settings given in [8] i.e. simulated binary crossover [6] with nc = 20 and the polynomial mutation operator with nm= 20. A crossover probability of 0.9 and a mutation probability of 0.15 are ....

K. Deb and T. Goel. Controlled elitist non-dominated sorting genetic algorithms for better convergence. In Proceedings of Evolutionary Multi-Criterion Optimization, pages 67-81, 2001.

....in an auxiliary population. For the maintenance of spread of solutions grid based techniques (PAES) clustering (SPEA) or crowding (NSGA II) were used. Further improve ments to these algorithms have also been proposed. NSGA II (Non dominated Sorting Genetic Algorithm II) with controlled elitism [5] limits the maximum number of individuals belonging to each front by a geometric decreasing function (governed by the reduction rate r) to introduce more diversity into the population. NSGA II, as reported in [8] outperformed PAES in preserving the spread of non dominated front on five ....

K. Deb and T. Goyal. Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence. KanGAL report 200004, Indian Institute of Technology, Kanpur, India, 2000.

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Deb, K. and Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence. In: Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization--Lecture Notes in Computer Science 1993, Zurich, Switzerland (2001) 67--81

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Kalyanmoy Deb and Tushar Goel. Controlled elitist non-dominated sorting genetic algorithm for better convergence. In Schoenauer et al. [SDR 00], pages 67-81.

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Deb K., Goel T. Controlled elitist non-dominated sorting genetic algorithms for better convergence. In Proceedings of Evolutionary Multi-Criterion Optimization. 67-81, 2001.

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