A genetic algorithm for continuous design space search

Rasheed, Khaled; Hirsh, Haym; Gelsey, Andrew

doi:10.1016/s0954-1810(96)00050-7

Cited by 83 publications

(41 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that for multiobjective GAs, maintaining diversity is a key issue. However we did not need to take any extra measures for diversity maintenance as the diversity maintenance module already present in GADO [1,2] seemed to handle this issue effectively. We focused on the case of two objectives in our experiments for simplicity of implementation and readability of the results, but the methods are applicable for multi-objective optimization problems with more than two objectives.…”

Section: Methods For Multi-objective Optimization Using Steady State Gasmentioning

confidence: 99%

“…We modified GADO [1,2] to create multi-objective OSGADO. OSGADO is inspired from the Vector Evaluated GA (VEGA) [9].…”

Section: Objective Switching Genetic Algorithm For Design Optimizatiomentioning

confidence: 99%

“…With good diversity maintenance, steady state GAs have done very well in several realistic domains [1]. Significant research has yet to be done in the area of steady state multi-objective GAs.…”

Section: Introductionmentioning

confidence: 99%

“…Our methods can be viewed as multi-objective transformations of GADO (Genetic Algorithm for Design Optimization) [1,2]. GADO is a GA that was designed with the goal of being suitable for the use in engineering design.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Chafekar

Jiang

Rasheed

2003

Genetic and Evolutionary Computation — GECCO 2003

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs. These methods are intended for solving real-world application problems that have many constraints and very small feasible regions. One method called Objective Exchange Genetic Algorithm for Design Optimization (OEGADO) runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with the others. The other method called Objective Switching Genetic Algorithm for Design Optimization (OSGADO) runs each objective sequentially with a common population for all objectives. Empirical results in benchmark and engineering design domains are presented. A comparison between our methods and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows that our methods performed better than NSGA-II for difficult problems and found Pareto-optimal solutions in fewer objective evaluations. The results suggest that our methods are better applicable for solving real-world application problems wherein the objective computation time is large.

show abstract

Section: Methods For Multi-objective Optimization Using Steady State Gasmentioning

confidence: 99%

“…We modified GADO [1,2] to create multi-objective OSGADO. OSGADO is inspired from the Vector Evaluated GA (VEGA) [9].…”

Section: Objective Switching Genetic Algorithm For Design Optimizatiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Chafekar

Jiang

Rasheed

2003

Genetic and Evolutionary Computation — GECCO 2003

Self Cite

View full text Add to dashboard Cite

show abstract

“…This process leads to the evolution of individuals and generate populations that are better suited to their environment. GAs are attractive in engineering design and applications because they are easy to use and are likely to find the globally best design or solution, which is superior to any other design or solution [10].…”

Section: Introductionmentioning

confidence: 99%

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

Manu

Kumar

2012

Phys. Rev. A

View full text Add to dashboard Cite

Experimental implementation of a quantum algorithm requires unitary operator decomposition. Here we treat the unitary operator decomposition as an optimization problem and use Genetic Algorithm, a global optimization method inspired by nature's evolutionary process for operator decomposition. As an application, we apply this to NMR Quantum Information Processing and find a probabilistic way of doing universal quantum computation using global hard pulses. We also demonstrate efficient creation of singlet state (as a special case of Bell state) directly from thermal equilibrium using an optimum sequence of pulses.

show abstract