Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Communications of the ACM
Self-Assembly of DNA-like Structures In Silico
Genetic Programming and Evolvable Machines
Crossover and mutation operators for grammar-guided genetic programming
Soft Computing - A Fusion of Foundations, Methodologies and Applications
RnaPredict—An Evolutionary Algorithm for RNA Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bioinformatics
The many facets of natural computing
Communications of the ACM
Evolving BlenX programs to simulate the evolution of biological networks
Theoretical Computer Science
Evolving biochemical reaction networks with stochastic attributes
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Automatic Design of DNA Logic Gates Based on Kinetic Simulation
DNA Computing and Molecular Programming
Embodied evolution and learning: the neglected timing of maturation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Evolutionary construction and adaptation of intelligent systems
Expert Systems with Applications: An International Journal
Swarm intelligence: the state of the art special issue of natural computing
Natural Computing: an international journal
Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing
IEEE Transactions on Evolutionary Computation
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This paper explores the synergies between evolutionary computation and synthetic biology, developing an in silico evolutionary system that is inspired by the behavior of bacterial populations living in continuously changing environments. This system creates a 3D environment seeded with a simulated population of bacteria that eat, reproduce, interact with each other and with the environment and eventually die. This provides a 3D framework implementing an evolutionary process. The subject of the evolution is each bacterium's internal process, defining its interactions with the environment. The evolutionary goal is the survival of the population under successive, continuously changing environmental conditions. The key advantage of this bacterial evolutionary system is its decentralized, asynchronous, parallel and self-adapting general-purpose evolutionary process. We describe this system and present the results of an application to the evolution of a bacterial population that learns how to predict the presence or absence of food in the environment by analyzing three input signals from the environment. The resulting populations successfully evolve by continuously improving their fitness under different environmental conditions, demonstrating their adaptability to a fluctuating medium.