Search results for "evolutionary computation"

Applications of Evolutionary Computation

2011

EvoCOMPLEX Contributions.- Coevolutionary Dynamics of Interacting Species.- Evolving Individual Behavior in a Multi-agent Traffic Simulator.- On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions.- Revising the Trade-off between the Number of Agents and Agent Intelligence.- Sexual Recombination in Self-Organizing Interaction Networks.- Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review.- EvoGAMES Contributions.- Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game.- Fuzzy Nash-Pareto Equilibrium: Concepts and Evolutionary Detection.- An Evolutionary Approach for Solving the Rubik's Cube Incorporating Exact Met…

Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder

2020

This paper presents the design, simulation and experimental verification of adaptive feedforward motion control for a hydraulic differential cylinder. The proposed solution is implemented on a hydraulic loader crane. Based on common adaptation methods, a typical electro-hydraulic motion control system has been extended with a novel adaptive feedforward controller that has two separate feedforward states, i.e, one for each direction of motion. Simulations show convergence of the feedforward states, as well as 23% reduction in root mean square (RMS) cylinder position error compared to a fixed gain feedforward controller. The experiments show an even more pronounced advantage of the proposed c…

DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization

2021

Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive …

Artificial Neural Networks to Predict the Power Output of a PV Panel

2014

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma m…

Evolutionary Algorithms and Metaheuristics : Applications in Engineering Design and Optimization

2018

A very brief history of soft computing: Fuzzy Sets, artificial Neural Networks and Evolutionary Computation

2013

This paper gives a brief presentation of history of Soft Computing considered as a mix of three scientific disciplines that arose in the mid of the 20th century: Fuzzy Sets and Systems, Neural Networks, and Evolutionary Computation. The paper shows the genesis and the historical development of the three disciplines and also their meeting in a coalition in the 1990s.

Artificial Neural Networks and Linear Discriminant Analysis:  A Valuable Combination in the Selection of New Antibacterial Compounds

2004

A set of topological descriptors has been used to discriminate between antibacterial and nonantibacterial drugs. Topological descriptors are simple integers calculated from the molecular structure represented in SMILES format. The methods used for antibacterial activity discrimination were linear discriminant analysis (LDA) and artificial neural networks of a multilayer perceptron (MLP) type. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval of the discriminant function and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the i…

A Study of Perceptron Mapping Capability to Design Speech Event Detectors

2006

Event detection is a fundamental yet critical component in automatic speech recognition (ASR) systems that attempt to extract knowledge-based features at the front-end level. In this context, it is common practice to design the detectors inside well-known frameworks based on artificial neural network (ANN) or support vector machine (SVM). In the case of ANN, speech scientists often design their detector architecture relying on conventional feed-forward multi-layer perceptron (MLP) with sigmoidal activation function. The aim of this paper is to introduce other ANN architectures inside the context of detection-based ASR. In particular, a bank of feed-forward MLPs using sinusoidal activation f…

Identification of the Parameters of Reduced Vector Preisach Model by Neural Networks

2008

This paper presents a methodology for identifying reduced vector Preisach model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using reduced vector Preisach model with preassigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.

An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks

2007

This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing the training of the neural network. This training is very challenging due to the large number of weights and noise caused by the dynamic neural network testing. The AGLMA is a memetic algorithm consisting of an evolutionary framework which adaptively employs two local searchers having different exploration logic and pivot rules. Furthermore, the AGLMA makes an adaptive noise compensation by means of explicit averaging on the fitness values and a dynamic population sizing which aims to follow the ne…