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Bioinformatics Research Based on Evolutionary Computation
Evolutionary computation-based association analysis has achieved significant progress in the field of data mining. This research approach fully...
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The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature survey
As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced impressive generative results by applying deep generative...
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Evolutionary Computation Meets Stream Processing
Evolutionary computation (EC) has a great potential of exploiting parallelization, a feature often underemphasized when describing evolutionary...
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Evolutionary Computation for Berth Allocation Problems: A Survey
Berth allocation problem (BAP) is to assign berthing spaces for incoming vessels while considering various constraints and objectives, which is an...
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A Privacy-Preserving Evolutionary Computation Framework for Feature Selection
Feature selection is a crucial process in data science that involves selecting the most effective subset of features. Evolutionary computation (EC)...
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A hybrid training algorithm based on gradient descent and evolutionary computation
Back propagation (BP) is widely used for parameter search of fully-connected layers in many neural networks. Although BP has the potential of quickly...
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A Double-Layer Reinforcement Learning Feature Optimization Framework for Evolutionary Computation Based Feature Selection Algorithms
Recently, Evolution Computing (EC) has gained widespread use in Feature Selection due to its powerful search capabilities. However, many EC...
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Evolutionary Dynamic Optimization and Machine Learning
Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature’s mechanisms of gradual development....
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An Innovative Evolutionary Computation Strategy for Optimizing Deep Learning Network
Deep learning is one of the subgroups of machine learning widely used in artificial intelligence (AI) fields such as remote sensing (RS) imagery and...
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A High-Performance Tensorial Evolutionary Computation for Solving Spatial Optimization Problems
As a newly emerged evolutionary algorithm, tensorial evolution (TE) has shown promising performance in solving spatial optimization problems owing to...
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Evolutionary Classification
Classification is a supervised machine learning process that categories an instance based on a number of features. The process of classification...
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A Collection of Robotics Problems for Benchmarking Evolutionary Computation Methods
The utilization of benchmarking techniques has a crucial role in the development of novel optimization algorithms, and also in performing comparisons...
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Evolutionary Regression and Modelling
Regression and modelling, which identify the relationship between the dependent and independent variables, play an important role in knowledge...
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A Novel Batch Framework-Based Performance Improvement of Evolutionary Algorithm
Evolutionary algorithms are widely used methodologies for problem-solving and are covered under the umbrella term of evolutionary computation (EC)....
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A survey on evolutionary computation for complex continuous optimization
Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies...
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Evolutionary Machine Learning in Robotics
In this chapter, we survey the most significant applications of EML to robotics. We first highlight the salient characteristics of the field in terms...
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Evolutionary spiking neural networks: a survey
Spiking neural networks (SNNs) are gaining increasing attention as potential computationally efficient alternatives to traditional artificial neural...
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Evolutionary Neural Network Architecture Search
Deep Neural Networks (DNNs) have been remarkably successful in numerous scenarios of machine learning. However, the typical design for DNN...
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Fundamentals of Evolutionary Machine Learning
In this opening chapter, we overview the quickly develo** field of evolutionary machine learning. We first motivate the field and define how we...