Genetic Algorithms and Artificial Life
نویسندگان
چکیده
Genetic algorithms are computational models of evolution that play a central role in many arti cial life models We review the history and current scope of research on genetic algorithms in arti cial life using illustrative examples in which the genetic algorithm is used to study how learning and evolution interact and to model ecosystems immune system cognitive systems and social systems We also outline a number of open questions and future directions for genetic algorithms in arti cial life research
منابع مشابه
Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms
Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Ne. As the first step for modeling, the empirical data is col...
متن کاملPareto Optimization of Two-element Wing Models with Morphing Flap Using Computational Fluid Dynamics, Grouped Method of Data handling Artificial Neural Networks and Genetic Algorithms
A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag ...
متن کاملComparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction
No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water...
متن کاملAppraisal of the evolutionary-based methodologies in generation of artificial earthquake time histories
Through the last three decades different seismological and engineering approaches for the generation of artificial earthquakes have been proposed. Selection of an appropriate method for the generation of applicable artificial earthquake accelerograms (AEAs) has been a challenging subject in the time history analysis of the structures in the case of the absence of sufficient recorded accelerogra...
متن کاملA New Hybrid model of Multi-layer Perceptron Artificial Neural Network and Genetic Algorithms in Web Design Management Based on CMS
The size and complexity of websites have grown significantly during recent years. In line with this growth, the need to maintain most of the resources has been intensified. Content Management Systems (CMSs) are software that was presented in accordance with increased demands of users. With the advent of Content Management Systems, factors such as: domains, predesigned module’s development, grap...
متن کاملPrediction of Surface Roughness by Hybrid Artificial Neural Network and Evolutionary Algorithms in End Milling
Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cut...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Artificial Life
دوره 1 شماره
صفحات -
تاریخ انتشار 1994