220 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Finally, facetwise models are developed to explore limitations of scalability of MOGAs, where the scalability of multiobjective algorithms in reliably maintaining Pareto-optimal solutions is addressed. The results show that even when the building blocks are accurately identified, massive multimodality of the search problems can easily overwhelm the nicher (diversity preserving operator) and lead to exponential scale-up. Facetwise models are developed, which incorporate the combined effects of model accuracy, decision making, and sub-structure supply, as well as the effect of niching on the population sizing, to predict a limit on the growth rate of a maximum number of su...
This paper investigates the problem of using a genetic algorithm to converge on a small, user-define...
Multiobjective evolutionary algorithms (MOEAs) are useful tools capable of searching problems that c...
This paper investigates the problem of using a genetic algorithm to converge on a small, user-define...
220 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Finally, facetwise models are...
Abstract- The paper analyzes the scalability of multiobjective estimation of distribution algorithms...
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving a...
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving a...
Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They appl...
Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scient...
In trying to solve multiobjective optimization problems, many traditional methods scalar-ize the obj...
This paper presents a new multiobjective genetic algorithm based on the Tchebycheff scalarizing func...
© 2015 Qiang Long et al. Multiobjective genetic algorithm (MOGA) is a direct search method for multi...
Practical optimization problems often have multiple objectives, which are likely to conflict with ea...
In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-...
Solving multi-objective linear programming and combinatorial optimization problems with search heuri...
This paper investigates the problem of using a genetic algorithm to converge on a small, user-define...
Multiobjective evolutionary algorithms (MOEAs) are useful tools capable of searching problems that c...
This paper investigates the problem of using a genetic algorithm to converge on a small, user-define...
220 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Finally, facetwise models are...
Abstract- The paper analyzes the scalability of multiobjective estimation of distribution algorithms...
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving a...
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving a...
Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They appl...
Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scient...
In trying to solve multiobjective optimization problems, many traditional methods scalar-ize the obj...
This paper presents a new multiobjective genetic algorithm based on the Tchebycheff scalarizing func...
© 2015 Qiang Long et al. Multiobjective genetic algorithm (MOGA) is a direct search method for multi...
Practical optimization problems often have multiple objectives, which are likely to conflict with ea...
In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-...
Solving multi-objective linear programming and combinatorial optimization problems with search heuri...
This paper investigates the problem of using a genetic algorithm to converge on a small, user-define...
Multiobjective evolutionary algorithms (MOEAs) are useful tools capable of searching problems that c...
This paper investigates the problem of using a genetic algorithm to converge on a small, user-define...