The proposal that evolution could be used as a metaphor for problem solving came with the invention of the computer 1. In the 1970s and 1980s the principal idea was developed into different ...

Evolutionary algorithms (EAs) represent a class of heuristic optimisation methods inspired by natural selection and Mendelian genetics. They iteratively evolve a population of candidate solutions ...

Amit Saha does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their ...

The field of optimization is confronting escalating challenges amid the growing scale, complexity, and dynamic nature of ...

Constantly "re-rolling the dice", combining and selecting: "Evolutionary algorithms" mimic natural evolution in silico and lead to innovative solutions for complex problems. Constantly “re-rolling the ...

“Uncountable organisms are working 24 hours a day, seven days a week to exploit new opportunities and overcome challenges that we put in their path. So the biological world is the most spectacular ...

With all the excitement over neural networks and deep-learning techniques, it’s easy to imagine that the world of computer science consists of little else. Neural networks, after all, have begun to ...

[Henrik] has been working on a program to design electronic circuits using evolutionary algorithms. It’s still very much a work in progress, but he’s gotten to the point of generating a decent BJT ...

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 ...

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 ...