G.O. Beale and G. Cook. Optimal Digital Simulation of Aircraft via Random Search Techniques. AIAA Journal of Guidance and Control, 1(4):237-241, 1978. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Using Unconstrained Elite Archives for Multi-Objective.. - Fieldsend, Everson, Singh (2001) (5 citations) (Correct)
....approach to solving these types of problem by using evolutionary search techniques. MOEAs have been in use for a considerable length of time now: Beale and Cook in 1978 used a random search technique in an attempt to simultaneously miniraise a number of objectives in an aircraft sim ulator [5]. However, it is the work of Schaffer in 1985 [6] which recognised the need to return a set of solutions, that has been widely quoted as the first MOEA study [7, 8, 9] The use of Evolutionary Algorithms (EAs) as the tool of choice is due to such problems being typically complex, with both a ....
....by the dominated tree method are significantly better than those found by clustering for all test functions except ZDT2, where there was no significant difference between the two methods. 7 STOPPING CRITERIA Robust stopping criteria are largely missing from the MOEA literature. Beale and Cook [5] include a fitness based stopping criterion in their study, in which the algorithm is terminated if the fittest individual has remained unchanged for 1000 consecutive generations. As Coello [10] points out, MOEAs since Schaffer [6] which carry a set of non dominated individuals, are usually ....
[Article contains additional citation context not shown here]
G.O. Beale and G. Cook. Optimal Digital Simulation of Aircraft via Random Search Techniques. AIAA Journal of Guidance and Control, 1(4):237-241, 1978.
Extensions to the Strength Pareto Evolutionary Algorithm. - Fieldsend, Everson, Singh (2001) (Correct)
.... techniques (usually, though not exclusively, by incorporating Genetic Algorithms (GAs) MOEAs have been in use for a considerable length of time now: Beale and Cook in 1978 used a random search technique in an attempt to simultaneously miniraise a number of objectives in an aircraft simulator [4]. However, it is the work of Schaffer in 1985 [5] which recognised the need to return a set of solutions, that has been widely quoted as the first MOEA study [6, 7, 8] The use of Evolutionary Algorithms (EAs) as the tool of choice is due to such problems being typically complex, with both a ....
....N extra individuals per generation. In this study N = 80, therefore the growth of the fastest growing frontal population (that of the four objective problem F5) is only a 40th of this rate. 7 STOPPING CRITERIA Robust stopping criteria are largely missing from the MOEA literature. Beale and Cook [4] include a fitness based stopping criterion in their study, in which the algorithm is terminated if the fittest individual has remained unchanged for 1000 consecutive generations. As Coello [9] points out, MOEAs since Schaffer [5] which carry a set of non dominated individuals, are usually ....
[Article contains additional citation context not shown here]
G.O. Beale and G. Cook. Optimal Digital Simulation of Aircraft via Random Search Techniques. AIAA Journal of Guidance and Control, 1(4):237-241, 1978.
Extensions to the Strength Pareto Evolutionary Algorithm. - Fieldsend, Everson, Singh (2001) (Correct)
.... techniques (usually, though not exclusively, by incorporating Genetic Algorithms (GAs) MOEAs have been in use for a considerable length of time now: Beale and Cook in 1978 used a random search technique in an attempt to simultaneously minimise a number of objectives in an aircraft simulator [4]. However, it is the work of Scha er in 1985 [5] which recognised the need to return a set of solutions, that has been widely quoted as the rst MOEA study [6, 7, 8] The use of Evolutionary Algorithms (EAs) as the tool of choice is due to such problems being 2 typically complex, with both a ....
....N extra individuals per generation. In this study N = 80, therefore the growth of the fastest growing frontal population (that of the four objective problem F5) is only a 40th of this rate. 7 STOPPING CRITERIA Robust stopping criteria are largely missing from the MOEA literature. Beale and Cook [4] include a tness based stopping criterion in their study, in which the algorithm is terminated if the ttest individual has remained unchanged for 1000 consecutive generations. As Coello [9] points out, MOEAs since Scha er [5] which carry a set of non dominated individuals, are usually ....
[Article contains additional citation context not shown here]
G.O. Beale and G. Cook. Optimal Digital Simulation of Aircraft via Random Search Techniques. AIAA Journal of Guidance and Control, 1(4):237241, 1978.
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