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It is natural to think of Evolutionary Algorithms as highly stochastic search methods. This can also make Evolutionary Algorithms, and particularly recombination, quite difficult to analyze. One way to reduce randomness involves the quadratization of ...
Evolutionary computation (EC) algorithms involve a careful collaborative and iterative update of a population of solutions to reach near a desired target. In a single-objective search and optimization problem, the respective optimal solution is often ...
Living organisms function according to protein circuits. Darwin's theory of evolution suggests that these circuits have evolved through variation guided by natural selection. However, it is currently unknown what variation mechanisms can give rise to ...
XCS constitutes the most deeply investigated classifier system today. It offers strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and ...
Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic ...
Substitution boxes (S-boxes) are nonlinear mappings that represent one of the core parts of many cryptographic algorithms (ciphers). If S-box does not possess good properties, a cipher would be susceptible to attacks. To design suitable S-boxes, we can ...
Genetic Programming for Symbolic Regression is often prone to overfit the training data, resulting in poor generalization on unseen data. To address this issue, many pieces of research have been devoted to regularization via controlling the model ...
Linkage learning is frequently employed in modern evolutionary algorithms. High linkage quality may be the key to an evolutionary method's effectiveness. Similarly, the faulty linkage may be the reason for its poor performance. Many state-of-the-art ...
Node-Depth Based Encoding is a representation for Evolutionary Algorithms applied to problems modelled by trees, storing nodes and their respective depths in an appropriately ordered list. This representation was highlighted by the results obtained, ...
Symbolic regression is a common application of genetic programming where model structure and corresponding parameters are evolved in unison. In the majority of work exploring symbolic regression, features are used directly without acknowledgement of ...
A Bilevel Optimization Problem (BOP) is related to two optimization problems in a hierarchical structure. A BOP is solved when an optimum of the upper level problem is found, subject to the optimal response of the respective lower level problem. This ...
Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy ...
Graph representations promise several desirable properties for Genetic Programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a ...
This paper presents a new method to automatically decompose general Mixed Integer Programs (MIPs). To do so, we represent the constraint matrix for a general MIP problem as a hypergraph and relax constraints by removing hyperedges from the hypergraph. A ...
We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diversity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse ...
Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification ...
Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of ...
Ensembles of classifiers have proved to be more effective than a single classification algorithm in skin image classification problems. Generally, the ensembles are created using the whole set of original features. However, some original features can be ...
In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques can be leveraged to efficiently deploy these compute-intensive ...
In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict the ...