evolutionary algorithms are among the most successful approaches for solving a number of problems where systematic searches in huge domains must be performed. One problem of practical interest that falls into this cat...
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evolutionary algorithms are among the most successful approaches for solving a number of problems where systematic searches in huge domains must be performed. One problem of practical interest that falls into this category is known as The Root Identification Problem in Geometric Constraint Solving, where one solution to the geometric problem must be selected among a number of possible solutions bounded by an exponential number. In previous works we have shown that applying genetic algorithms, a category of evolutionary algorithms, to solve the Root Identification Problem is both feasible and effective. In this work, we report on an empirical statistical study conducted to establish the influence of the driving parameters in the PBIL and CHC evolutionary algorithms when they are used to solve the Root Identification Problem. We identify a set of values that optimize algorithms performance. The driving parameters considered for the PBIL algorithm are population size, mutation probability, mutation shift and learning rate. For the CHC algorithm we studied population size, divergence rate, differential threshold and the set of best individuals. In both cases we applied unifactorial and multifactorial analysis, post hoc tests and best parameter level selection. Experimental results show that CHC outperforms PBIL when applied to solve the Root Identification Problem. (C) 2010 Elsevier B.V. All rights reserved.
Many large combinatorial optimization problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This fact forces programmers to use clusters of c...
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Many large combinatorial optimization problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This fact forces programmers to use clusters of computers, a computational solution very useful for running applications of intensive calculus but having a high acquisition price and operation cost, mainly due to the Central Processing Unit (CPU) power consumption and refrigeration devices. A low-cost and high-performance alternative comes from reconfigurable computing, a hardware technology based on Field Programmable Gate Array devices (FPGAs). The main objective of the work presented in this paper is to compare implementations on FPGAs and CPUs of different fitness functions in evolutionary algorithms in order to study the performance of the floating-point arithmetic in FPGAs and CPUs that is often present in the optimization problems tackled by these algorithms. We have taken advantage of the parallelism at chip-level of FPGAs pursuing the acceleration of the fitness functions (and consequently, of the evolutionary algorithms) and showing the parallel scalability to reach low cost, low power and high performance computational solutions based on FPGA. Finally, the recent popularity of GPUs as computational units has moved us to introduce these devices in our performance comparisons. We analyze performance in terms of computation times and economic cost.
The subject of this research is the automated startup procedure of a PI state-controlled rolling-mill motor by using evolutionary algorithms, Compared to the conventional PI speed control, applying the method of delib...
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The subject of this research is the automated startup procedure of a PI state-controlled rolling-mill motor by using evolutionary algorithms, Compared to the conventional PI speed control, applying the method of deliberate pole placement to the state controller design succeeds in improving the transient response of setpoint and disturbance changes. To put the PI state-controlled drive with observer into operation to obtain a controller with a high robustness and dynamics, the precise knowledge of this physical parameter is necessary. An evolution-based system is used to solve the estimation problem, A high degree of reliability respecting multimodal characteristics and robustness against random noise is expected from the identification method. Evulotionary algorithms fulfill this requirement. With genetic operators like mutation, crossover, and selection, evolutionary algorithms mimic the principles of organic evolution in order to solve the optimization problem.
This paper addresses the Network-on-Chip (NoC) application mapping problem. This is an NP-hard problem that deals with the optimal topological placement of Intellectual Property cores onto the NoC tiles. Network-on-Ch...
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This paper addresses the Network-on-Chip (NoC) application mapping problem. This is an NP-hard problem that deals with the optimal topological placement of Intellectual Property cores onto the NoC tiles. Network-on-Chip application mapping evolutionary algorithms are developed, evaluated and optimized for minimizing the NoC communication energy. Two crossover and one mutation operators are proposed. It is analyzed how each optimization algorithm performs with every genetic operator, in terms of solution quality and convergence speed. Our proposed operators are compared with state-of-the-art genetic operators for permutation problems. Finally, the problem is approached in a multi-objective way. Besides energy minimization, it is also aimed to map the cores such that a thermally balanced Network-on-Chip design is obtained. It is shown, through simulations on real applications, that by using domain-knowledge, our developed genetic operators increase the algorithms' performance. By comparing these evolutionary algorithms with an Optimized Simulated Annealing, it is shown that they perform better. In the case of two contradictory objectives, our genetic operators can still help at providing the mappings with the lowest communication energy. (c) 2012 Elsevier B.V. All rights reserved.
The discovery of new sounds can inspire creativity, and various approaches have been explored in that effort. One approach is the application of evolutionary algorithms to sound synthesis. The work presented here is r...
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The discovery of new sounds can inspire creativity, and various approaches have been explored in that effort. One approach is the application of evolutionary algorithms to sound synthesis. The work presented here is related to those efforts and focuses on the properties of pattern-producing networks and neuroevolution. The authors chronicle a journey of concept development and implementation iterations. A discussion of a browser-based environment facilitating such explorations is followed by an account of a migration to a more flexible execution technology stack, using the Web Audio API. As a result of that process, the authors introduce a system consisting of a software library and a command-line utility that facilitates further investigations. Those products are intended to serve as tools for further explorations of the applicability of evolutionary algorithms to the synthesis and discovery of inspiring sounds.
In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the opt...
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In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions. (C) 2010 Elsevier Ltd. All rights reserved.
The ability to solve inventive problems is at the core of the innovation process: however, the standard procedure to deal with them is to utilize random trial and error, despite the existence of several theories and m...
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The ability to solve inventive problems is at the core of the innovation process: however, the standard procedure to deal with them is to utilize random trial and error, despite the existence of several theories and methods. TRIZ and evolutionary algorithms (EA) have shown results that support the idea that inventiveness can be understood and developed systematically. This article presents a strategy based on dialectical negation in which both approaches converge, creating a new conceptual framework for enhancing computer-aided problem solving. Two basic ideas presented are the inversion of the traditional EA selection ("survival of the fittest"), and the incorporation of new dialectical negation operators in evolutionary algorithms based on TRIZ principles. Two case studies are the starting point to discuss what kind of results can be expected using this "Dialectical Negation Algorithm" (DNA). (C) 2010 Elsevier B.V. All rights reserved.
The effect of power-law fitness scaling method on the convergence and distribution of MOEAs is investigated in a systematic fashion. The proposed method is named as gamma (gamma) correction-based fitness scaling (GCFS...
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The effect of power-law fitness scaling method on the convergence and distribution of MOEAs is investigated in a systematic fashion. The proposed method is named as gamma (gamma) correction-based fitness scaling (GCFS). What scaling does is that the selection pressure of a population can be efficiently regulated. Hence, fit and unfit individuals may be separated well in fitness-wise before going to the selection mechanism. It is then applied to Strength Pareto evolutionary Algorithm 2 (SPEA2) and Domination Power of an individual Genetic Algorithm (DOPGA). Firstly, the effectiveness of GCFS is tested by 11 static gamma values (including 0.5, 1, 2, ..., 9, 10) on nine well-known benchmarks. Simulated study safely states that SPEA2 and DOPGA may perform generally better with the square (gamma = 2) and the cubic (gamma = 3) of original fitness value, respectively. Secondly, an adaptive version of GCFS is proposed based on statistical merits (standard deviation and mean of fitness values) and implemented to the selected MOEAs. Generally speaking, fitness scaling significantly improves the convergence properties of MOEAs without extra computational burdens. It is observed that the convergence ability of existing MOEAs with fitness scaling (static or adaptive) can be improved. Simulated results also show that GCFS is only effective when fitness proportional selection methods (such as stochastic universal sampling-SUS) are used. GCFS is not effective when tournament selection is used.
In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users'...
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In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message delivery rate depends both on the users' mobility patterns and the message load in the network. This new type of configuration, however, poses new challenges to security, amongst them, assessing the effect that a group of colluding malicious participants can have on the global message delivery rate in such a network is far from trivial. In this work, after modeling such a question as an optimization problem, we are able to find quite interesting results by coupling a network simulator with an evolutionary algorithm. The chosen algorithm is specifically designed to solve problems whose solutions can be decomposed into parts sharing the same structure. We demonstrate the effectiveness of the proposed approach on two medium-sized Delay-Tolerant Networks, realistically simulated in the urban contexts of two cities with very different route topology: Venice and San Francisco. In all experiments, our methodology produces attack patterns that greatly lower network performance with respect to previous studies on the subject, as the evolutionary core is able to exploit the specific weaknesses of each target configuration. (C) 2015 Elsevier B.V. All rights reserved.
From the viewpoint of "FUBEN-EKI," we have developed an inconvenient input method for mobile devices, which provides us with chances of exploration. In the proposed mobile device, users provide a large numbe...
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