Evolutionary computation comprises a family of metaheuristic algorithms inspired by the principles of natural evolution – reproduction, mutation, recombination, and selection – which are utilised to ...

Genetic algorithms borrow their name and principles from biological evolution, but can they help researchers discover the fundamentals of life? Evolution is one of the most widely known theories in ...

A professor recently developed an evolutionary computation approach that offers researchers the flexibility to search for models that can best explain experimental data derived from many types of ...

Evolutionary reinforcement learning is an exciting frontier in machine learning, combining the strengths of two distinct approaches: reinforcement learning and evolutionary computation. In ...

We were introduced to plotting numbers on a graph early in our secondary year schooling. The usual first step was to draw the horizontal and vertical lines that represent the x- and the y-axis. This ...

Designed for courses on Evolutionary Multi-objective Optimization and Evolutionary Algorithms. This textbook is the second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, ...

At the intersection of neuroscience and artificial intelligence (AI) is an alternative approach to deep learning. Evolutionary algorithms (EA) are a subset of evolutionary computation—algorithms that ...

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It turns out that 155 years after Charles Darwin first published “On the Origin of Species,” vexing questions remain about key aspects of evolution, such as how sexual recombination and natural ...

Evolutionary computation (EC) incorporates evolutionary ideas into algorithms. These algorithms can be applied to problems of biological interest. They are interesting models for evolution, so that EC ...