Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints
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Linear functions have gained a lot of attention in the area of run time analysis of evolutionary computation methods and the corresponding analyses have provided many effective tools for analyzing more complex problems. In this paper, we consider the behavior of the classical (1+1) Evolutionary Algorithm for linear functions under linear constraint. We show tight bounds in the case where both the objective and the constraint function is given by the OneMax function and present upper bounds as well as lower bounds for the general case. We also consider the LeadingOnes fitness function.Fitness evaluation is a basic operation in evolutionary computation. However, an explicit fitness function is not always available in real-world applications. In many cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a short survey on fitness approximation in evolutionary computation is given. Main issues like approximation models, model management schemes, learning methods as well as data sampling techniques are presented. Finally, interesting open problems are discussed.
Fitness approximation
Human-based evolutionary computation
Function Approximation
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Association rule analysis has been widely employed as a basic technique for data mining. Extensive research has also been conducted to apply evolutionary computing techniques to the field of data mining. This study presents a method to evaluate the settings of evolutionary operations in evolutionary rule discovery method, which is characterized by the execution of overall problem solving through the acquisition and accumulation of small results. Since the purpose of population evolution is different from that of general evolutionary computation methods that aim at discovering elite individuals, we examined the difference in the concept of settings during evolution and the evaluation of evolutionary computation by visualizing the progress and efficiency of problem solving. The rule discovery method (GNMiner) is characterized by reflecting acquired information in evolutionary operations; this study determines the relationship between the settings of evolutionary operations and the progress of each task execution stage and the achievement of the final result. This study obtains knowledge on the means of setting up evolutionary operations for efficient rule-set discovery by introducing an index to visualize the efficiency of outcome accumulation. This implies the possibility of setting up dynamic evolutionary operations in the outcome accumulation-type evolutionary computation in future studies.
Human-based evolutionary computation
Evolutionary music
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Evolutionary computation comes from the natural evolution of evolutionary thinking and inspiration. Parallel to its potential and self-organizing, adaptive, self-learning smart features for solving multi-objective optimization problems with great potential. Systematically introduces the multi-objective evolutionary algorithms to optimize multi-objective evolutionary algorithm (MOEAs), at the same time discusses the evolutionary algorithms (EAs) in the multi-objective optimization of the application of a number of key issues in the future and the need for further research work.
Human-based evolutionary computation
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We use an evolutionary algorithm in which we change the fitness function periodically to model the fact that objectives can change during creative problem solving. We performed an experiment to observe the behavior of the evolutionary algorithm regarding its response to these changes and its ability to successfully generate solutions for its creative task despite the changes. An analysis of the results of this experiment sheds some light into the conditions under which the evolutionary algorithm can respond with varying degrees of robustness to the changes.
Robustness
Fitness approximation
Algorithm design
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Some interesting features of the new book "Introduction to Evolutionary Algorithms", which is written by Xinjie Yu and Mitsuo Gen and be published by Springer in 2010, will be illustrated, including covering nearly all the hot evolutionary-computation-related topics, referring to the latest published journal papers, introducing the applications of EAs as many as possible, and adopting many pedagogical ways to make EAs easy and interesting. The contents and the consideration of selecting these contents will be discussed. Then the focus will be put on the pedagogical ways for teaching and self-study. The algorithms introduced in the tutorial include constrained optimization evolutionary algorithms (COEA), multiobjective evolutionary algorithms (MOEA), ant colony optimization (ACO), particle swarm optimization (PSO), and artificial immune systems (AIS). Some of the applications of these evolutionary algorithms will be discussed.
Human-based evolutionary computation
Parallel metaheuristic
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Bound‐constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound‐constrained benchmark functions and various real‐world problems. In general, the developed evolutionary algorithms (EAs) belong to nature‐inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well‐known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty‐nine large‐scale bound‐constrained benchmark functions. The suggested C‐DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE‐CEC benchmark functions are tackled efficiently in terms of proximity and diversity.
Benchmark (surveying)
Differential Evolution
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Human-based evolutionary computation
Margin (machine learning)
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Interactive evolutionary computation (IEC), which takes a user's evaluation as a fitness function, performs poorly for local search due to the limitation of population size and generation length. To solve this, the direct manipulation (DM) method, well known in HCI, of evolution for IEC has been proposed. It allows the user to manipulate individuals directly, instead of using evolutionary operators as an interface to each individual. In this paper, we analyze the usefulness of DM with a fitness landscape and N-K model. We have applied the DM concept to a fashion design system based on IEC, and analyzed the results with the concept of fitness landscape and Boolean hypercube.
Fitness landscape
Fitness approximation
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The difference between interactive evolution-ary computation (IEC) and traditional evolutionary computation (TEC) is that in IEC individuals' fitness is subjectively assigned by the user, while in TEC the fitness is objectively given by function or others. The user in IEC assigns fitness according to his/her preference. Therefore, if his/her preference drifts, the implicit fitness function for preference would also drift. This will cause the invalidation of the history evolutionary information such as individuals fitness and so on. In this situation, it is the key issue that how to make use of the history evolutionary information to accelerate evolving without misleading the evolutionary direction. The gradual drift of the user's preference is mainly studied in this paper. The definition of gradual drift of the user's preference is given and the methods to make use of the history evolutionary information are put forward. At last, the experiments show the efficiency of these methods.
Fitness approximation
Human-based evolutionary computation
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