• Corpus ID: 108555079

Speciation, clustering and other genetic algorithm improvements for structural topology optimization

  title={Speciation, clustering and other genetic algorithm improvements for structural topology optimization},
  author={James W. Duda},
  • J. Duda
  • Published 1996
  • Computer Science
Genetic algorithms are used to search for optimal structural topologies. Modifications to basic genetic algorithm techniques are implemented to increase computational efficiency, avoid premature convergence to a single solution, and solve new categories of problems. GA's are a search and optimization tool based on the principles of evolution and survival of the fittest. Potential designs are represented by chromosomes, each of which receives a fitness score based on the quality of the design it… 

Cosmic Inflation and Genetic Algorithms

In this part, the genetic programming approach has the potential to uncover a multitude of viable inflationary models with new functional forms and more complex methods of search relying on reinforcement learning and genetic programming are explored.



Structural topology optimization via the genetic algorithm

The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology optimization by evolving a population of "chromosomes," where each chromosome creates a potentially-optimal structure topology.

Genetic algorithms as an approach to configuration and topology design

The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design and methods for mapping genetic algorithm “chromosomes” into this representation are detailed.

An Investigation of Niche and Species Formation in Genetic Function Optimization

This contribution briefly describes problems preventing niche formation in conventional genetic algorithms, a crowding method for niche formation and analysis of results when optimizing two multimodal functions.

Niching methods for genetic algorithms

Why crowding methods over the last two decades have not made effective niching methods is determined and a series of tests and design modifications results in the development of a highly effective form of crowding, called deterministic crowding.

Genetic algorithm-based structural topology design with compliance and topology simplification considerations

This article compares the genetic-algorithm-based technique with homogenization methods in the minimization of a structure’s compliance subject to a maximum volume constraint and uses the technique to generate topologies combining high structural performance with a variety of material connectivity characteristics which arise directly from the discretized design representation.

Handbook Of Genetic Algorithms

This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.

Structural Topology Optimization in Linear and Nonlinear Elasticity Using Genetic Algorithms

In this paper, structural topology optimization is addressed through Genetic Algorithms: a set of designs is evolved following the Darwinian survival-of-ttest principle to optimize the weight of the structure under displacement constraints.

Genetic programming - on the programming of computers by means of natural selection

  • J. Koza
  • Computer Science
    Complex adaptive systems
  • 1993
This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.

Optimization of Control Parameters for Genetic Algorithms

  • J. Grefenstette
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics
  • 1986
GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.