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 ...

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 ...

Evolutionary algorithms (EAs) have long provided a flexible framework for solving challenging optimisation problems by mimicking natural evolutionary processes. When combined with multitask ...

“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 ...

The goal of a combinatorial optimization problem is to find a set of distinct integer values that minimizes some cost function. The most famous example is the Traveling Salesman Problem (TSP). There ...

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

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 ...

Imagine a very complex problem—supply chain optimization, for example—in which a computer generates millions of trial solutions completely at random and then ...

April 15 (UPI) --Scientists have developed a new algorithm that can predict how a protein could evolve to become highly effective or totally unproductive. The machine learning model -- detailed this ...