Decomposition based algorithms perform well when a suitable set of weights are provided;however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowle...
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Decomposition based algorithms perform well when a suitable set of weights are provided;however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowledge about the geometry of the problem. This study proposes a novel algorithm called preference-inspired co-evolutionary algorithm using weights (PICEA-w) in which weights are co-evolved with candidate solutions during the search process. The co-evolution enables suitable weights to be constructed adaptively during the optimisation process, thus guiding candidate solutions towards the Pareto optimal front effectively. The benefits of co-evolution are demonstrated by comparing PICEA-w against other leading decomposition based algorithms that use random, evenly distributed and adaptive weights on a set of problems encompassing the range of problem geometries likely to be seen in practice, including simultaneous optimisation of up to seven conflicting objectives. Experimental results show that PICEA-w outperforms the comparison algorithms for most of the problems and is less sensitive to the problem geometry. (C) 2014 Elsevier B.V. All rights reserved.
Two evolutionary algorithms (EAs) are assessed in this paper to design optimal operating rules (ORs) for Water Resource Systems (WRS). The assessment is established through a parameter analysis of both algorithms in a...
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Two evolutionary algorithms (EAs) are assessed in this paper to design optimal operating rules (ORs) for Water Resource Systems (WRS). The assessment is established through a parameter analysis of both algorithms in a theoretical case, and the methodology described in this paper is applied to a complex, real case. These two applications allow us to analyse an algorithm's properties and performance by defining ORs, how an algorithm's termination/convergence criteria affect the results and the importance of decision-makers participating in the optimisation process. The former analysis reflects the need for correctly defining the important algorithm parameters to ensure an optimal result and how the greater number of termination conditions makes the algorithm an efficient tool for obtaining optimal ORs in less time. Finally, in the complex real case application, we discuss the participation value of decision-makers toward correctly defining the objectives and making decisions in the post-process. (C) 2014 Elsevier Ltd. All rights reserved.
In this paper, neuro-fuzzy based group method of data handling (NF-GMDH) as an adaptive learning network was utilized to predict the local scour depth at pile groups under clear-water conditions. The NF-GMDH network w...
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In this paper, neuro-fuzzy based group method of data handling (NF-GMDH) as an adaptive learning network was utilized to predict the local scour depth at pile groups under clear-water conditions. The NF-GMDH network was developed using particle swarm optimization (PSO) and gravitational search algorithm (GSA). Effective parameters on the scour depth include bed sediment size, geometric properties, piles spacing, arrangements of pile group, and flow characteristics in upstream of group piles and critical flow condition due to initiation of particles' motion on bed surface. Nine dimensional parameters were considered to define a functional relationship between input and output variables. The NF-GMDH models were carried out using datasets collected from the literature. The efficiency of training stages for both NF-GMDH-PSO and NF-GMDH-GSA models was investigated. Testing results for the NF-GMDH networks were compared with the empirical equations. The NF-GMDH-PSO network produced more efficient performance (R=0.95 and RMSE=0.035) for scour depth prediction compared with the NF-GMDH-GSA model (R=0.94 and RMSE=0.036). The NF-GMDH models indicated quite higher accuracy of scour prediction, compared with the empirical equations (R=0.44 and RMSE = 0.127). Also, the sensitivity analysis indicated that pier diameter was the most significant parameter on scour depth. (C) 2015 Elsevier Ltd. All rights reserved.
evolutionary algorithms (EAs) excel in optimizing systems with a large number of variables. Previous mathematical and empirical studies have shown that opposition-based algorithms can improve EA performance. We review...
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evolutionary algorithms (EAs) excel in optimizing systems with a large number of variables. Previous mathematical and empirical studies have shown that opposition-based algorithms can improve EA performance. We review existing opposition-based algorithms and introduce a new one. The proposed algorithm is named fitness-based quasi-reflection and employs the relative fitness of solution candidates to generate new individuals. We provide the probabilistic analysis to prove that among all the opposition-based methods that we investigate, fitness-based quasi-reflection has the highest probability of being closer to the solution of an optimization problem. We support our theoretical findings via Monte Carlo simulations and discuss the use of different reflection weights. We also demonstrate the benefits of fitness-based quasi-reflection on three state-of-the-art EAs that have competed at IEEE CEC competitions. The experimental results illustrate that fitness-based quasi-reflection enhances EA performance, particularly on problems with more challenging solution spaces. We found that competitive DE (CDE) which was ranked tenth in CEC 2013 competition benefited the most from opposition. CDE with fitness-based quasi-reflection improved on 21 out of the 28 problems in the CEC 2013 test suite and achieved 100% success rate on seven more problems than CDE. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimiz...
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In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm. (C) 2015 Elsevier B.V. All rights reserved.
Room temperature Fourier transform infrared spectra of the four-membered heterocycle trimethylene sulfide were collected with a resolution of 0.00096 cm(-1) using synchrotron radiation from the Canadian Light Source f...
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Room temperature Fourier transform infrared spectra of the four-membered heterocycle trimethylene sulfide were collected with a resolution of 0.00096 cm(-1) using synchrotron radiation from the Canadian Light Source from 500 to 560 cm(-1). The in-plane ring deformation mode (nu(13)) at similar to 529 cm(-1) exhibits dense rotational structure due to the presence of ring inversion tunneling and leads to a doubling of all transitions. Preliminary analysis of the experimental spectrum was pursued via traditional methods involving assignment of quantum numbers to individual transitions in order to conduct least squares fitting to determine the spectroscopic parameters. Following this approach, the assignment of 2358 transitions led to the experimental determination of an effective Hamiltonian. This model describes transitions in the P and R branches to J' = 60 and K-a' = 10 that connect the tunneling split ground and vibrationally excited states of the nu(13) band although a small number of low intensity features remained unassigned. The use of evolutionary algorithms (EA) for automated assignment was explored in tandem and yielded a set of spectroscopic constants that re-create this complex experimental spectrum to a similar degree. The EA routine was also applied to the previously well-understood ring puckering vibration of another four-membered ring, azetidine (Zaporozan et al., 2010). This test provided further evidence of the robust nature of the EA method when applied to spectra for which the underlying physics is well understood. (C) 2015 Elsevier Inc. All rights reserved.
In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm...
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In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm optimization and the principles of an evolutionary process. We then propose a hybrid fuzzy rough with K-nearest neighbor (K-NN)-based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bio-inspired algorithm search. While exploring other possible hybrid evolutionary processes, we then conducted experiments considering (i) same feature selection algorithm with support vector machine (SVM) and random forest (RF) classifier;(ii) instance based selection using synthetic minority over-sampling technique with fuzzy rough K-nearest neighbor (K-NN), SVM and RF classifier. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.
Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by ...
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Swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by employing a population-based approach in mutual communication and information sharing and processing, including randomness. In this paper, history of swarm and evolutionary algorithms are discussed in general as well as their dynamics, structure and behavior. The core of this paper is an overview of an alternative way how dynamics of arbitrary swarm and evolutionary "algorithms can be visualized, analyzed and controlled. Also selected representative applications are discussed at the end. Both subtopics are based on interdisciplinary intersection of two interesting research areas: swarm and evolutionary algorithms and complex dynamics of nonlinear systems that usually exhibit very complex behavior. (C) 2015 Elsevier B.V. All rights reserved.
Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existi...
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Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existing individuals in order to generate new potentially better fit offspring leading to more promising areas of the search space. In this paper, we describe and analyze the best-order recombination operator for permutation-based encoding. The proposed operator uses genetic information from the two parents and from the best individual obtained up to the current generation. These sources of information are integrated to determine the best order of values in the new permutation. In order to evaluate the performance of best-order crossover, we address three well-known -hard optimization problems i.e. Travelling Salesman Problem, Vehicle Routing Problem and Resource-Constrained Project Scheduling Problem. For each of these problems, a set of benchmark instances is considered in a comparative analysis of the proposed operator with eight other crossover schemes designed for permutation representation. All crossover operators are integrated in the same standard evolutionary framework and using the same parameter setting to allow a comparison focused on the recombination process. Numerical results emphasize a good performance of the proposed crossover scheme which is able to lead to overall better quality solutions.
The ubiquitous presence of proteins in chemical pathways in the cell and their key role in many human disorders motivates a growing body of protein modeling studies to unravel the relationship between protein structur...
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The ubiquitous presence of proteins in chemical pathways in the cell and their key role in many human disorders motivates a growing body of protein modeling studies to unravel the relationship between protein structure and function. The foundation of such studies is the realization that knowledge of the structures a protein accesses under physiological conditions is key to a detailed understanding of its biological function and the design of therapeutic compounds for the purpose of altering misfunction in aberrant variants of a protein. Dry laboratory investigations promise a holistic treatment of the relationship between protein sequence, structure, and function. Significant efforts are made in the dry laboratory to map protein conformation spaces and underlying energy landscapes of proteins. The majority of such efforts employ well-studied computational templates, such as Molecular Dynamics and Monte Carlo. The focus of this review is on a third emerging template, stochastic optimization under the umbrella of evolutionary computation. algorithms based on such a template, also known as evolutionary algorithms, are showing promise in addressing fundamental computational challenges in protein structure modeling and are opening up new avenues in protein modeling research. This review summarizes evolutionary algorithms for novice readers, while highlighting recent developments that showcase current, state-of-the-art capabilities for *** ubiquitous presence of proteins in chemical pathways in the cell and their key role in many human disorders motivates a growing body of protein modeling studies to unravel the relationship between protein structure and function. The foundation of such studies is the realization that knowledge of the structures a protein accesses under physiological conditions is key to a detailed understanding of its biological function and the design of therapeutic compounds for the purpose of altering misfunction in aberrant variants of a prot
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