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GPEM 18(1) is available

The first issue of Volume 18 of Genetic Programming and Evolvable Machines is now available for download. This is a special issue on Genetic Improvement, edited by Justyna Petke, and it also contains three book reviews. The complete contents are: “Edit… Continue reading

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IEEE Transactions on Evolutionary Computation information for authors

Presents institutional listings relating to this publication. Continue reading

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Automatically Evolving Rotation-Invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain expert who, in… Continue reading

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IEEE World Congress on Computational Intelligence

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A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling … Continue reading

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The Effect of Information Utilization: Introducing a Novel Guiding Spark in the Fireworks Algorithm

The fireworks algorithm (FWA) is a competitive swarm intelligence algorithm which has been shown to be very useful in many applications. In this paper, a novel guiding spark (GS) is introduced to further improve its performance by enhancing the information utilization in the FWA. The idea is to use the objective function’s information acquired by explosion sparks to construct a guiding vector (GV) with promising direction and adaptive length, and to generate an elite solution called a GS by adding the GV to the position of the firework. The FWA with GS is called the guided FWA (GFWA). Experimental results show that the GS contributes greatly to both exploration and exploitation of the GFWA. The GFWA outperforms previous versions of the FWA and other swarm and evolutionary algorithms on a large variety of test functions and it is also a useful method for large scale optimization. The principle of the GS is very simple but efficient, which can be easily transplanted to other population-based algorithms. Continue reading

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Efficient Use of Partially Converged Simulations in Evolutionary Optimization

For many real-world optimization problems, evaluating a solution involves running a computationally expensive simulation model. This makes it challenging to use evolutionary algorithms that usually have to evaluate thousands of solutions before converg… Continue reading

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Artificial Immune System With Local Feature Selection for Short-Term Load Forecasting

In this paper, a new forecasting model based on artificial immune system (AIS) is proposed. The model is used for short-term electrical load forecasting as an example of forecasting time series with multiple seasonal cycles. Artificial immune system le… Continue reading

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Ranking Vectors by Means of the Dominance Degree Matrix

In multi-/many-objective evolutionary algorithms (MOEAs), there are varieties of vector ranking schemes, including nondominated sorting, dominance counting, and so on. Usually, these vector ranking schemes in the classical MOEAs are of high computation… Continue reading

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Table of contents

Presents the table of contents for this issue of the publication. Continue reading

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