evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity...
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ISBN:
(纸本)9781577357384
evolutionary algorithms have been frequently used for dynamic optimization problems. With this paper, we contribute to the theoretical understanding of this research area. We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very effective in dynamically tracking changes made to the problem instance.
As users can have greatly different preferences, the personalization of ambient devices is of utmost importance. Several approaches have been proposed to establish such a personalization in the form of machine learnin...
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In many applications of evolutionary algorithms, the time required to evaluate the fitness of individuals is long and variable. When the variance in individual evaluation times is non-negligible, traditional, synchron...
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A recent trend in evolutionary algorithms (EAs) transfers expertise from and to other areas of machine learning. An interesting novel symbiosis considers: i) reinforcement learning (RL), which learns on-line and off-l...
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ISBN:
(纸本)9781450334884
A recent trend in evolutionary algorithms (EAs) transfers expertise from and to other areas of machine learning. An interesting novel symbiosis considers: i) reinforcement learning (RL), which learns on-line and off-line difficult dynamic elaborated tasks requiring lots of computational resources, and ii) EAs with the main strength its eloquence and computational efficiency. These two techniques address the same problem of reward maximization in difficult environments that can include stochasticity. Sometimes, they exchange techniques in order to improve their theoretical and empirical efficiency, like computational speed for on-line learning, and robust behaviour for the off-line optimisation algorithms. For example, multi-objective RL uses tuples of rewards instead of a single reward value and techniques from multi-objective EAs should be integrated for an efficient exploration/exploitation trade-off. The problem of selecting the best genetic operator is similar to the problem an agent faces when choosing between alternatives in achieving its goal of maximising its cumulative expected reward. Practical approaches select the RL method that solve the best online operator selection problem.
This paper proposes an improved performance metric for multiobjective evolutionary algorithms with user preferences. This metric uses the idea of decomposition to transform the preference information into m+1 points o...
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The design of software architectures requires to address a number of competing non-functional properties (NFPs): improving one NFP requires to degrade another one. As a consequence, software architects have to come up...
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This paper proposes to tackle the structure design and operation of a Micro-Grid in a jointly way, by means of a novel nested evolutionary algorithms (EAs) approach. Specifically, in an scenario of variable electricit...
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evolutionary algorithm theory has studied the time complexity of evolutionary algorithms for more than 20 years. Different aspects of this rich and diverse research field were presented in four different advanced or s...
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ISBN:
(纸本)9781450334884
evolutionary algorithm theory has studied the time complexity of evolutionary algorithms for more than 20 years. Different aspects of this rich and diverse research field were presented in four different advanced or specialized tutorials at last year's GECCO. This tutorial presents the foundations of this field. We introduce the most important notions and definitions used in the field and consider different evolutionary algorithms on a number of well-known and important example problems. Through a careful and thorough introduction of important analytical tools and methods, including fitness-based partitions, typical events and runs and drift analysis, by the end of the tutorial the attendees will be able to apply these techniques to derive relevant runtime results for non-trivial evolutionary algorithms. Moreover, the attendees will be fully prepared to follow the more advanced tutorials that cover more specialized aspects of the field, including the new advanced runtime analysis tutorial on realistic population-based EAs. To assure the coverage of the topics required in the specialised tutorials, this introductory tutorial will be coordinated with the presenters of the more advanced ones. In addition to custom-tailored methods for the analysis of evolutionary algorithms we also introduce the relevant tools and notions from probability theory in an accessible form. This makes the tutorial appropriate for everyone with an interest in the theory of evolutionary algorithms without the need to have prior knowledge of probability theory and analysis of randomized algorithms. The last two editions of this tutorial at GECCO 2013 and GECCO 2014 attracted over 50 participants each.
evolutionary algorithms (EAs), or evolutionary Computation, are powerful algorithms that have been used in a range of challenging real-world problems. In this paper, we are interested in their applicability on a dynam...
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