Evolving neural networks through augmenting topologies
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Matplotlib: A 2D Graphics Environment
Computing in Science and Engineering
Automated synthesis of mechanical vibration absorbers using genetic programming
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Human-competitive results produced by genetic programming
Genetic Programming and Evolvable Machines
Python: An Ecosystem for Scientific Computing
Computing in Science and Engineering
Computer-automated evolution of an x-band antenna for nasa's space technology 5 mission
Evolutionary Computation
Modeling the evolutionary dynamics of plasmids in spatial populations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Over the past few decades, evolutionary computation (EC) has grown substantially in use for biologists and engineers alike. Its transparency makes it an indispensable tool for studying evolutionary- and ecological dynamics, and it has provided researchers with new insights that would be tremendously difficult, if not impossible, to gain using natural systems. In addition, EC has proven to be a powerful search algorithm for engineering applications, and has produced numerous novel and human-competitive solutions to complex problems. Although several well-established packages are readily available, it seems that when most users harness the power of evolutionary computation, they do so using "home-grown" solutions. This can likely be attributed to the ease with which simple models are created, the user's need for customization, and the sizeable learning barrier imposed by available solutions, as well as difficulties in extending them. We present SEEDS, a modular, open-source platform for conducting evolutionary computation experiments. SEEDS provides a simple, flexible, and extensible foundation that enables users with minimal programming experience to perform complex evolutionary and ecological simulations without having to first implement core functionality. In addition, SEEDS provides the tools necessary to make sharing data and reproducing experiments both easy and convenient.