This paper reports work investigating various evolutionary approaches to vertex cover (VC), a well-known NP-Hard optimization problem. Central to each of the algorithms is a novel encoding scheme for VC and related pr...
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A graph is edge-biconnected if it requires the removal of at least two edges to disconnect it. Assume that we have weighted graph that is not biconnected, and an additional set of augmentation edges. The (NP-hard) edg...
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A graph is edge-biconnected if it requires the removal of at least two edges to disconnect it. Assume that we have weighted graph that is not biconnected, and an additional set of augmentation edges. The (NP-hard) edge biconnectivity augmentation problem is to select a minimal subset of the augmentation edges, whose inclusion will cause the graph to be biconnected. This paper explores the application of particle swarm optimization and genetic algorithms for this problem.
This paper investigates groundwater system characterization problem, in this inverse problem the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optim...
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作者：
Toscano, R.Lyonnet, P.Université de Lyon
Laboratoire de Tribologie et de Dynamique des Systémes CNRS UMR5513 ECL/ENISE 58 rue Jean Parot 42023 Saint-Etienne Cedex 2 France
In this paper we introduce an extension of standard geometric programming (GP) problems which we call quasi geometric programming (QGP) problems. The consideration of this particular kind of nonlinear and possibly non...
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ISBN:
(纸本)9789898425317
In this paper we introduce an extension of standard geometric programming (GP) problems which we call quasi geometric programming (QGP) problems. The consideration of this particular kind of nonlinear and possibly non smooth optimization problem is motivated by the fact that many engineering problems can be formulated as a QGP. However, solving a QGP remains a difficult task due to its intrinsic non-convex nature. This is why we investigate the possibility of using evolutionary algorithms (EA) for solving a QGP problem. The main idea developed in this paper is to combine evolutionary algorithms with interior point method for efficiently solving QGP problems. An interesting feature of the proposed approach is that it does not need to develop specific program solver and works well with any existing EA and available solver able to solve conventional GP. Some considerations on the robustness issue are also presented. Numerical experiments are used to validate the proposed method.
In the last two decades, great progress has been made in molecular modeling through computational treatments of biological molecules grounded in evolutionary search techniques. evolutionary algorithms (EAs) are gainin...
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ISBN:
(纸本)9781450334884
In the last two decades, great progress has been made in molecular modeling through computational treatments of biological molecules grounded in evolutionary search techniques. evolutionary algorithms (EAs) are gaining popularity beyond exploring the relationship between sequence and function in biomolecules. In particular, recent work is showing the promise of EAs in exploring structure spaces of protein chains to address open problems in computational structural biology, such as de novo structure prediction and other structure modeling problems. Exploring effective interleaving of global and local search has led to hybrid EAs that are now competitive with the Monte Carlo-based frameworks that have traditionally dominated de novo structure prediction. Deeper understanding of the constraints posed by highly-coupled modular systems like proteins and integration of domain knowledge have resulted in effective reproductive operators. Multi-objective optimization has also shown promise in dealing with the conflicting terms that make up protein energy functions and effectively exploring protein energy surfaces. Combinations of these techniques have recently resulted in powerful stochastic search frameworks that go beyond de novo structure prediction and are capable of yielding comprehensive energy landscapes containing possible diverse functionally-relevant structures of proteins. The objective of this tutorial is to introduce the EC community to the rapid developments on EA-based frameworks for protein structure modeling through a concise but comprehensive review of developments in this direction over the last decade. The review will be accompanied with specific detailed highlights and interactive software demonstrations of representative methods. Building on the success and feedback of a related tutorial presented by the organizers at GECCO 2014, highlights will focus on de novo structure prediction and then energy landscape mapping of wildtype and disease-causing varia
All evolutionary algorithms experienced practitioners emphasiz the need for a careful design of the fitness function. It is commonly heard, for instance, that "If there is a bug in your fitness function, the EA w...
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With the rapid development of next-generation sequencing and high-throughput technologies, much biological data have been generated. The analysis of biological networks is becoming a hot topic in bioinformatics in rec...
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This text introduces a family of evolutionary algorithms named EPSO - evolutionary Particle Swarm Optimization. EPSO algorithms are evolutionary methods that borrow the movement rule from Particle Swarm Optimization m...
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The Capacitated Arc Routing Problem (CARP) involves vehicles routing, serving a set of arcs in a network. This NP hard problem is extended to take into account time windows, entailing a new and hard theoretical model ...
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Bridges are vital links in infrastructure road networks and require frequent maintenance and repair to keep them functional throughout their service lives. However, with most existing bridges being old and the funds a...
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ISBN:
(纸本)0784407940
Bridges are vital links in infrastructure road networks and require frequent maintenance and repair to keep them functional throughout their service lives. However, with most existing bridges being old and the funds available for repair being limited, the prioritization of bridges for repair, the allocation of the limited funds, and the selection of appropriate repair methods become complex optimization decisions. This is still true even when considering only one bridge component (e.g., deck) within a large network of bridges. In this paper, an integrated bridge deck management system is formulated with detailed life cycle cost analysis. The system's implementation on a spreadsheet program is briefly highlighted. Five evolutionary algorithms namely;genetic algorithms, memetic algorithms, particle swarm, ant colony systems, and shuffled frog leaping are then introduced and applied to optimize maintenance and repair decisions for various problems with different numbers of bridges. Based on the results obtained, the benefits of both the model formulation and the use of evolutionary algorithms are discussed, and the most suitable algorithm is selected for the proposed bridge deck management system. This paper contributes not only to the development of advanced management systems that can be adapted to various infrastructure types, but also to the implementation of new techniques for large scale optimization.
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