Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware ...
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ISBN:
(纸本)9781479967803
Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed optimization algorithm for creating optimized regression suites from verification stimuli that were evaluated in simulation-based verification environment. The results of our experiments show that the size of the final regression suites are significantly improved in comparison to the original test suit. For our experimental system, we were able to eliminate 94.4% redundant stimuli from the original test suite while retaining the same level of coverage.
evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack probl...
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ISBN:
(纸本)9783319992532;9783319992525
evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every tau iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by tau and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.
We analyze the performance of panmictic evolutionary algorithms in byzantine environments in which fitness can be computed by malicious agents. We measure the influence of the rate of unreliability of the environment,...
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ISBN:
(纸本)9798400701207
We analyze the performance of panmictic evolutionary algorithms in byzantine environments in which fitness can be computed by malicious agents. We measure the influence of the rate of unreliability of the environment, and the effect that a simple mechanism based on redundant computation can have on the results attained.
During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a proble...
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During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem from being used on others. In this article, a formal, conceptual framework is developed to unify the design and implementation of EAs for many geographical optimization problems. The key element in this framework is a graph representation that defines the spatial structure of a broad range of geographical problems. Based on this representation, four types of geographical optimization problems are discussed and a set of algorithms is developed for problems in each type. These algorithms can be used to support the design and implementation of EAs for geographical optimization. Knowledge specific to geographical optimization problems can also be incorporated into the framework. An example of solving political redistricting problems is used to demonstrate the application of this framework.
Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an evolutionary Algorithm component, ...
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ISBN:
(纸本)9783319116839;9783319116822
Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.
Relaxed forms of Pareto dominance have been shown to be the most effective way in which evolutionary algorithms can progress towards the Pareto-optimal front with a widely spread distribution of solutions. A popular c...
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ISBN:
(纸本)9783642198922
Relaxed forms of Pareto dominance have been shown to be the most effective way in which evolutionary algorithms can progress towards the Pareto-optimal front with a widely spread distribution of solutions. A popular concept is the epsilon-dominance technique, which has been employed as an archive update strategy in some multiobjective evolutionary algorithms. In spite of the great usefulness of the epsilon-dominance concept, there are still difficulties in computing an appropriate value of epsilon that provides the desirable number of nondominated points. Additionally, several viable solutions may be lost depending on the hypergrid adopted, impacting the convergence and the diversity of the estimate set. We propose the concept of cone epsilon-dominance, which is a variant of the epsilon-dominance, to overcome these limitations. Cone epsilon-dominance maintains the good convergence properties of epsilon-dominance, provides a better control over the resolution of the estimated Pareto front, and also performs a better spread of solutions along the front. Experimental validation of the proposed cone epsilon-dominance shows a significant improvement in the diversity of solutions over both the regular Pareto-dominance and the epsilon-dominance.
Multi-method and multi-operator evolutionary algorithms (EAs) have shown superiority to any single EAs with a single operator. To further improve the performance of such algorithms, in this research study, a united mu...
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ISBN:
(纸本)9781479914883
Multi-method and multi-operator evolutionary algorithms (EAs) have shown superiority to any single EAs with a single operator. To further improve the performance of such algorithms, in this research study, a united multi-operator EAs framework is proposed, in which two EAs, each with multiple search operators, are used. During the evolution process, the algorithm emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on a well-known set of constrained problems with 10D and 30D. The results show that the proposed algorithm scales well and is superior to the-state-of-the-art algorithms, especially for the 30D test problems.
There is a lot of experimental evidence that crossover is, for some functions, an essential operator of evolutionary algorithms. Nevertheless, it was an open problem to prove for some function that an evolutionary alg...
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ISBN:
(纸本)3540662510
There is a lot of experimental evidence that crossover is, for some functions, an essential operator of evolutionary algorithms. Nevertheless, it was an open problem to prove for some function that an evolutionary algorithm using crossover is essentially more efficient than evolutionary algorithms without crossover. In this paper, such an example is presented and its properties are proved.
Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes that are used in every evolutionary algorithm like genetic algorithms e...
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ISBN:
(纸本)9783319071725;9783319071732
Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes that are used in every evolutionary algorithm like genetic algorithms etc. In this paper we present experiments (based on our previous) of selected evolutionary algorithms and test functions demonstrating impact of non-random generators on performance of the evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions Griewangk and Rastrigin. We use n periodical deterministic processes (based on deterministic chaos principles) instead of pseudorandom number generators and compare performance of evolutionary algorithms powered by those processes and by pseudorandom number generators. Results presented here has to be understand like numerical demonstration rather than mathematical proofs. Our results (reported sooner and here) suggest hypothesis that certain class of deterministic processes can be used instead of random number generators without lowering the performance of evolutionary algorithms.
In this paper we analyze a new method for an adaptive variation of evolutionary algorithms (EAs) population size: the Self-Regulated Population size EA (SRP-EA). An empirical evaluation of the method is provided by co...
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ISBN:
(纸本)3540389903
In this paper we analyze a new method for an adaptive variation of evolutionary algorithms (EAs) population size: the Self-Regulated Population size EA (SRP-EA). An empirical evaluation of the method is provided by comparing the new proposal with the CHC algorithm and other well known EAs with varying population. A fitness landscape generator was chosen to test and compare the algorithms: the Spear's multimodal function generator. The performance of the algorithms was measured in terms of success rate, quality of the solutions and evaluations needed to attain them over a wide range of problem instances. We will show that SRP-EA performs well on these tests and appears to overcome some recurrent drawbacks of traditional EAs which lead them to local optima premature convergence. Also, unlike other methods, SRP-EA seems to self-regulate its population size according to the state of the search.
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