Open AccessJournal Article
Simulated Binary Crossover for Continuous Search Space.
TLDR
A real-coded crossover operator is developed whose search power is similar to that of the single-point crossover used in binary-coded GAs, and SBX is found to be particularly useful in problems having mult ip le optimal solutions with a narrow global basin where the lower and upper bo unds of the global optimum are not known a priori.Abstract:
Abst ract . T he success of binary-coded gene t ic algorithms (GA s) in problems having discrete sear ch space largely depends on the coding used to represent the prob lem var iables and on the crossover ope ra tor that propagates buildin g blocks from parent strings to children st rings . In solving optimization problems having continuous search space, binary-coded GAs discr et ize the search space by using a coding of the problem var iables in binary strings. However , t he coding of realvalued vari ables in finit e-length st rings causes a number of difficulties: inability to achieve arbit rary pr ecision in the obtained solution , fixed mapping of problem var iab les, inh eren t Hamming cliff problem associated wit h binary coding, and processing of Holland 's schemata in cont inuous search space. Although a number of real-coded GAs are developed to solve optimization problems having a cont inuous search space, the search powers of these crossover operators are not adequate . In t his paper , t he search power of a crossover operator is defined in terms of the probability of creating an arbitrary child solut ion from a given pair of parent solutions . Motivated by the success of binarycoded GAs in discrete search space problems , we develop a real-coded crossover (which we call the simulated binar y crossover , or SBX) operator whose search power is similar to that of the single-point crossover used in binary-coded GAs . Simulation results on a nu mber of realvalued test problems of varying difficulty and dimensionality suggest t hat the real-cod ed GAs with the SBX operator ar e ab le to perfor m as good or bet ter than binary-cod ed GAs wit h the single-po int crossover. SBX is found to be particularly useful in problems having mult ip le optimal solutions with a narrow global basin an d in prob lems where the lower and upper bo unds of the global optimum are not known a priori. Further , a simulation on a two-var iable blocked function shows that the real-coded GA with SBX work s as suggested by Goldbergread more
Citations
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The Adaptive Robust Design Approach : Improving Analytical Support under Deep Uncertainty
TL;DR: A new methodological approach for improving analytical support for policymaking under deep uncertainty is developed, and each analytical advancement stage is demonstrated with case studies and the effectiveness of ARD for developing adaptive robust policies underDeep uncertainty is shown by illustrative case studies.
Journal ArticleDOI
A dominance tree and its application in evolutionary multi-objective optimization
TL;DR: A data structure for storing the whole population and their dominating information in MOEAs, called a Dominance Tree (DT), is a binary tree that can effectively and efficiently store three-valued relations (namely dominating, dominated or non-dominated) among vector values.
Proceedings ArticleDOI
An efficient multi-objective evolutionary algorithm with steady-state replacement model
Dipti Srinivasan,L. Rachmawati +1 more
TL;DR: A Multi-objective Evolutionary Algorithm which avoids Pareto-ranking altogether by employing the transitivity of the domination relation is proposed, which performs favorably compared to the popular NSGA-II in terms of convergence as well as diversity of the Pare to-set approximation, and is computationally more efficient.
Patent
Evolutionary search for robust solutions
TL;DR: In this paper, the authors present a method for optimizing a parameter set comprising object parameters, the method comprising the steps of: (a) creating an initial population of a plurality of individual parameter sets, the parameter sets comprising object parameter describing a model, structure, shape, design or process to be optimized and setting the initial population as a current parent population; (b) for each individual parameter set in a parent population mutating the parameters and optionally recombining the parameters to create an offspring population, wherein the strength of an individual object parameter mutation is enlarged by a noise contribution to enhance
Journal ArticleDOI
Topology optimization of compliant structures and mechanisms using constructive solid geometry for 2-d and 3-d applications
TL;DR: A new software framework involving optimization routine for geometry and mesh generation with FEA solver has been developed for handling voids, non-design constraints, and irregular boundary shapes of the design domain, which are critical for any structural optimization.
References
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Book
Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution
A Survey of Evolution Strategies.
TL;DR: Evolution Strategies are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems and adaptation of the strategy parameters for the mutation variances as well as their covariances are described.
Journal Article
Genetic algorithms, noise, and the sizing of populations
TL;DR: Results suggest how the sizing equation may be viewed as a coarse delineation of a boundary between what a physicist might call two distinct phases of GA behavior, and how these results may one day lead to rigorous proofs of convergence for recombinative G As operating on problems of bounded description.
Forma Analysis and Random Respectful Recombination.
TL;DR: Intrinsic parallelism is shown to have application beyond schemata and o-schemata and more general objects called formae are introduced and general operators which manipulate these are introduced.