Abstract
Evolutionary algorithms have been quite effective in dealing with single-objective “optimization” while the area of Evolutionary Multiobjective Optimization (EMOO) has extended its efficiency to Multiple Criteria Decision Making (MCDM) as well. The number of technical publications in EMOO is impressive and indicative of a rather explosive growth in recent years. It is fair to say however that most of the progress has been in applying and evolving algorithms and their convergence properties, not in evolving the optimality concept itself, nor in expanding the notions of true optimization. Yet, the conceptual constructs based on evolution and Darwinian selection have probably most to contribute – at least in theory – to the evolution of optimality. They should be least dependent on a priori fixation of anything in problem formulation: constraints, objectives or alternatives. Modern systems and problems are typical for their flexibility, not for their fixation. In this paper we draw attention to the impossibility of optimization when crucial variables are given and present Eight basic concepts of optimality. In the second part of this contribution we choose a more realistic problem of linear programming where constraints are not “given” but flexible and to be optimized and objective functions are multiple: De novo programming.
Keywords
- Multiple Criterion
- Maximal Solution
- Single Criterion
- Multiple Criterion Decision
- Darwinian Selection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zeleny, M. (2005). The Evolution of Optimality: De Novo Programming. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_1
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DOI: https://doi.org/10.1007/978-3-540-31880-4_1
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