Property Valuations in Times of Crisis: Artificial Neural Networks and Evolutionary Algorithms in Comparison

2015 ◽  
pp. 194-209 ◽  
Author(s):  
Francesco Tajani ◽  
Pierluigi Morano ◽  
Marco Locurcio ◽  
Nicola D’Addabbo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Artificial Neural

Evolutionary Approaches for ANNs Design

2011 ◽  
pp. 575-580
Author(s):  
Antonia Azzini ◽  
Andrea G.B. Tettamanzi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Computational Models ◽  
Artificial Neurons ◽  
Important Research Topic ◽  
Recent Developments ◽  
Evolving System ◽  
Artificial Neural ◽  
Ann Application

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A particular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologicallyinspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs. Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006). The aim of this article is to present the main evolutionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.

Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives

2013 ◽  
Vol 26 (8) ◽  
pp. 1781-1794 ◽  
Author(s):  
Alexandru-Ciprian Zăvoianu ◽  
Gerd Bramerdorfer ◽  
Edwin Lughofer ◽  
Siegfried Silber ◽  
Wolfgang Amrhein ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Multi Objective ◽  
Electrical Drives ◽  
Artificial Neural

scholarly journals Evolutionary Robotics: A New Scientific Tool for Studying Cognition

2005 ◽  
Vol 11 (1-2) ◽  
pp. 79-98 ◽  
Author(s):  
Inman Harvey ◽  
Ezequiel Di Paolo ◽  
Rachel Wood ◽  
Matt Quinn ◽  
Elio Tuci
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Evolutionary Robotics ◽  
Minimal Models ◽  
Close Coupling ◽  
Essential Aspect ◽  
Sensorimotor Systems ◽  
Artificial Neural ◽  
Scientific Tool

We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments, which is an essential aspect of real cognition that is often either bypassed or modeled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion, the origins of learning, and the ontogenetic acquisition of entrainment.

Multi-Objective Optimization of Parameters for Milling Using Evolutionary Algorithms and Artificial Neural Networks

2019 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu-Mahfouz ◽  
AHM Esfakur Rahman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Cost Function ◽  
Machine Learning Algorithms ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Artificial Neural

Abstract Model-based design of manufacturing processes have been gaining popularity since the advent of machine learning algorithms such as evolutionary algorithms and artificial neural networks (ANN). The problem of selecting the best machining parameters can be cast an optimization problem given a cost function and by utilizing an input-output connectionist framework using as ANNs. In this paper, we present a comparison of various evolutionary algorithms for parameter optimization of an end-milling operation based on a well-known cost function from literature. We propose a modification to the cost function for milling and include an additional objective of minimizing surface roughness and by using NSGA-II, a multi-objective optimization algorithm. We also present comparison of several population-based evolutionary search algorithms such as variants of particle swarm optimization, differential evolution and NSGA-II.

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