Artificial immune systems (AIS) are randomised search heuristics that can be applied to any kind of optimisation problem just like evolutionary algorithms (EAs). Unlike EAs their inception stems from a different natur...
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ISBN:
(纸本)9781450342063
Artificial immune systems (AIS) are randomised search heuristics that can be applied to any kind of optimisation problem just like evolutionary algorithms (EAs). Unlike EAs their inception stems from a different natural paradigm: the immune system of vertebrates instead of natural evolution. While AIS proved to be highly efficient in several application areas, so far for no classic optimisation problem it could be rigorously proven that AIS outperform EAs. We consider the B-Cell Algorithm (BCA) as an example of an artificial immune system and compare its performance with that of the (1+1) EA on a classic combinatorial optimisation problem, interval scheduling. We show that for the natural binary encoding both heuristics are not able to efficiently find an optimal solution for all instances. The (1+1) EA has exponential expected optimisation time and the BCA may never find an optimal solution. However, we also show that a natural preprocessing changes the situation dramatically. If we remove beforehand all intervals strictly containing another one, then the BCA always finds an optimal solution in polynomial expected optimisation time. The (1+1) EA remains inefficient in the worst case, still having exponential expected optimisation time. It can, however, find a 2-approximation of an optimal solution very quickly.
evolutionary algorithms provide reasonable speed in time for solving optimization problems. However, these approaches may stay insufficient when the problem gets bigger and needs more hardware resource. Because it is ...
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ISBN:
(纸本)9781509016792
evolutionary algorithms provide reasonable speed in time for solving optimization problems. However, these approaches may stay insufficient when the problem gets bigger and needs more hardware resource. Because it is not feasible to improve memory and computation power depending on the problem size, there is a need for developing new techniques in order to solve these optimization problems in less time with smaller error ratios. In this study, it is aimed to solve the Traveling Salesman Problem, one of the NP-complete complexity problems, by partitioning the problem with a clustering technique, K-Means, and solving these pieces with Genetic Algorithm and finally combining these solutions into one. As the experimental results suggest, in comparison to solving large scale optimization problems as single problems, solving them by partitioning them yields more convincing results in both solution quality and time. In addition, it is observed that the performance of the technique yields better as the problem size gets bigger.
The reduction of energy consumption is a major challenge around the world. Architectural aspects have a significant place to minimize energy consumption to the maximum level. The use of large glazed facades causes ove...
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ISBN:
(纸本)9781509006229
The reduction of energy consumption is a major challenge around the world. Architectural aspects have a significant place to minimize energy consumption to the maximum level. The use of large glazed facades causes overheating problems in certain climatic regions. Shading elements must be considered at an early stage in the design process to overcome this problem. An application of the method is presented, focusing on the horizontal louvers integrated to a building in Izmir, Turkey. The contributions of the paper can be summarized as follows. We show that most architectural design problems are basically real-parameter multi-objective constrained optimization problems. So, any type of evolutionary and swarm optimization methods can be used in this field. A multi-objective self-adaptive differential evolution algorithm (jDEMO), inspired from the DEMO algorithm from the literature with some modifications, is developed and compared to the well-known fast and nondominated sorting genetic algorithm so called NSGA-II in order to solve this complex problem and identify alternative design solutions to decision makers. Through the experimental results, we show that the proposed algorithm generated slightly better results when comparing to the NSGA-II algorithm.
Factored evolutionary algorithms (FEA) have proven to be fast and efficient optimization methods, often outperforming established methods using single populations. One restriction to FEA is that it requires a central ...
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ISBN:
(纸本)9781450342063
Factored evolutionary algorithms (FEA) have proven to be fast and efficient optimization methods, often outperforming established methods using single populations. One restriction to FEA is that it requires a central communication point between all of the factors, making FEA difficult to use in completely distributed settings. The Distributed Factored evolutionary Algorithm (DFEA) relaxes this requirement on central communication by having neighboring factors communicate directly with one another. While DFEA has been effective at finding good solutions, there is often an increase in computational complexity due to the communication between factors. In previous work on DFEA, the authors required the algorithm reach full consensus between factors during communication. In this paper, we demonstrate that even without full consensus, the performance of DFEA was not statistically different on problems with low epistasis. Additionally, we found that there is a relationship between the convergence of consensus between factors and the convergence of fitness of DFEA.
Energy consumption is a matter of paramount importance in nowadays environmentally conscious society. It is also bound to be a crucial issue in light of the emergent computational environments arising from the pervasi...
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ISBN:
(纸本)9783319458236;9783319458229
Energy consumption is a matter of paramount importance in nowadays environmentally conscious society. It is also bound to be a crucial issue in light of the emergent computational environments arising from the pervasive use of networked handheld devices and wearables. evolutionary algorithms (EAs) are ideally suited for this kind of environments due to their intrinsic flexibility and adaptiveness, provided they operate on viable energy terms. In this work we analyze the energy requirements of EAs, and particularly one of their main flavours, genetic programming (GP), on several computational platforms and study the impact that parametrisation has on these requirements, paving the way for a future generation of energy-aware EAs. As experimentally demonstrated, handheld devices and tiny computer models mainly used for educational purposes may be the most energy efficient ones when looking for solutions by means of EAs.
Ensembles of classifiers is a way to improve the performance of the approach with single classifiers. The idea is to find and combine a set of classifiers that are responsible for smaller and theoretically easier part...
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ISBN:
(纸本)9781509035663
Ensembles of classifiers is a way to improve the performance of the approach with single classifiers. The idea is to find and combine a set of classifiers that are responsible for smaller and theoretically easier parts of a problem to solve, in other words, divide to conquer. Between the ensembles models, there is the clustering and selection in which the training data are clustering, and a classifier is built for each cluster found. An answer for an input data is given based on a distance to the available clusters that has an associated classifier. In this paper, the clustering and selection model is explored with the use of evolutionary algorithms to search clusters that optimize the ensemble's performance. Experiments are conducted with ten datasets and using recent advances in classification methods. The results achieved good and promising performances compared to classical clustering-and-selection model and other methods to build ensembles.
evolutionary algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., ine...
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ISBN:
(纸本)9781450343237
evolutionary algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches such as the use of tournament rules based on feasibility, multiobjective optimization concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative search engines (e.g., particle swarm optimization, differential evolution, evolution strategies, etc.) will be also discussed (as time allows).
Online shopping has become an essential part of our life, which provides a suitable, cheap, and quick way for customers to enjoy a wide variety of products. However, due to the large number of online stores, a custome...
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ISBN:
(纸本)9781509035496
Online shopping has become an essential part of our life, which provides a suitable, cheap, and quick way for customers to enjoy a wide variety of products. However, due to the large number of online stores, a customer usually faces difficulties to review all available offers manually in order to find a favorite item. The Internet shopping optimization problem (ISOP) is a multiple-item multiple-shop optimization problem, which targets to minimize the total cost for a costumer to purchase a given set of products over all available offers. In this paper, the mathematical model of existing ISOP has been improved. In the improved model of ISOP different constraints and assumptions such as the maximum budget, discounts offered by internet shops have been taken into account. Several metaheuristic optimization methods such as the genetic algorithm are implemented. The obtained numerical results illustrate the effectiveness of the improved model and metaheuristics applied.
Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to pro...
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ISBN:
(纸本)9783319312040
Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowledge evolutionary algorithms (EAs) have so far not been used for this setting, due to the success of DP. In this work we compare DP and EA for a well-studied example and find that for the basic scenario EA is indeed clearly outperformed by DP in terms of calculation time and quality of solutions. But, we also find that when going beyond the standard scenario towards more realistic (and complex) scenarios, EAs can actually deliver a performance en par or in some cases even exceeding DP, making them useful in a number of relevant application scenarios.
Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex di...
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Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. In this paper, two evolutionary methods were compared to detect associations of single nucleotide polymorphisms (SNPs): a genetic algorithm and Gauss particle swarm optimization. Genetic algorithm was developed with partial matched crossover operator and two different strategies for initialization: regular initialization and top-5 strategy initialization. In both methods for different SNP barcodes (SNP combinations with their corresponding genotypes) the difference between case and control data is computed systematically. The algorithms look for the best combination which is the barcode with maximum difference between the two groups. Analysis results support that the genetic algorithm with top-5 strategy for initialization provides higher frequency difference values than the Gauss particle swarm optimization. It is also proved that a genetic algorithm reduces a computational cost for obtaining higher frequency difference between the case and control group. (C) 2016 The Authors. Published by Elsevier B.V.
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