It is not unusual that an approximate model is needed for fitness evaluation in evolutionary computation. In this case, the convergence properties of the evolutionary algorithm are unclear due to the approximation error of the model. In this paper, extensive empirical studies are carried out to investigate the convergence properties of an evolution strategy using an approximate fitness function on two benchmark problems. It is found that incorrect convergence will occur if the approximate model has false optima. To address this problem, individual- and generation-based evolution control are introduced and the resulting effects on the convergence properties are presented. A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization, which is able to guarantee the correct convergence of the evolutionary algorithm, as well as to reduce the computation cost as much as possible. Control of the evolution and updating of the approximate models are based on the estimated fidelity of the approximate model. Numerical results are presented for three test problems and for an aerodynamic design example.

A framework for evolutionary optimization with approximate fitness functions

The generic denomination of ‘Memetic Algorithms’ (MAs) is used to encompass a broad class of metaheuristics (i.e. general purpose methods aimed to guide an underlying heuristic). The method is based on a population of agents and proved to be of practical success in a variety of problem domains and in particular for the approximate solution of NP Optimization problems. Unlike traditional Evolutionary Computation (EC) methods, MAs are intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature that characterizes MAs. This functioning philosophy is perfectly illustrated by the term “memetic”. Coined by R. Dawkins [52], the word ‘meme’ denotes an analogous to the gene in the context of cultural evolution [154]. In Dawkins’ words:

/pdf/a-gentle-introduction-to-memetic-algorithms-44dcqfqqiz.pdf

A Gentle Introduction to Memetic Algorithms

Methods and systems for optimizing a solution set. A solution set is generated, and solutions in the solution set are evaluated. Desirable solutions from the solution set are selected. A structural model is created using the desirable solutions, and a surrogate fitness model is created based on the structural model and the desirable solutions. A new solution set may be generated and/or evaluated, based on analyzing at least one of the structural model and the surrogate fitness model, and determining a method for generating a new solution set and/or evaluating the new solution set based at least in part on the analyzing.

Adaptive optimization methods

Memetic algorithms (MAs) are optimization techniques based on the synergistic combination of ideas taken from other metaheuristics, such as populational search (as in evolutionary techniques), and local search (as in gradient-ascent techniques). This work shows the general structure of MAs, and provides some guidelines for their design. Furthermore, an overview of the numerous applications of these techniques is presented. The paper finishes with an sketch of current trends in the development of the area.

An Introduction to Memetic Algorithms

Los algoritmos memeticos (MA) son tecnicas de optimizacion que combinan sinergicamente conceptos tomados de otras metaheuristicas, tales como la busqueda basada en poblaciones (como en los algoritmos evolutivos), y la mejora local (como en las tecnicas de seguimiento del gradiente). En este trabajo se muestra la anatoma general de un MA, y se proporcionan pautas para el diseno de los mismos. Asimismo,se muestra una panoramica general del amplio abanico de aplicaciones que tienen estas tecnicas. El trabajo concluye bosquejando algunos de los aspectos de mayor interes actual en el desarrollo del area.

Una Introducción a los Algoritmos Memeticos

The thermal generator maintenance scheduling problem has been tackled by a variety of traditional optimisation techniques over the years. While these methods can give an optimal solution to small scale problems, they are often inefficient and impractical when applied to larger problems. We employ a multi-stage approach where the problem is decomposed into smaller sub-problems, each of which can be solved much more efficiently by existing algorithms. After each part of the problem has been solved, the results are then recombined to form the solution to the whole problem. Both tabu search and a memetic algorithm have been observed to produce very good results but they take a significant amount of time to run. We utilise both techniques to form the basis of a multi-stage approach to solve the thermal generator maintenance scheduling problem. The results demonstrate that the multi-stage methodology is just as effective for this problem while achieving a significant reduction in run-time.

A multi-stage approach for the thermal generator maintenance scheduling problem

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

A.J. Smith

Author Tools

Chat about Author

Papers

A multi-stage approach for the thermal generator maintenance scheduling problem