Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Adaptive populations of endogenously diversifying Pushpop organisms are reliably diverse
ICAL 2003 Proceedings of the eighth international conference on Artificial life
breve: a 3D environment for the simulation of decentralized systems and artificial life
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Emergence of Collective Behavior in Evolving Populations of Flying Agents
Genetic Programming and Evolvable Machines
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic Programming Theory and Practice III (Genetic Programming)
Genetic Programming Theory and Practice III (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automated shape composition based on cell biology and distributed genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
EvoSpace: a distributed evolutionary platform based on the tuple space model
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Evolving a digital multiplier with the pushgp genetic programming system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Expressive genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The success of a genetic programming system in solving a problem is often a function of the available computational resources. For many problems, the larger the population size and the longer the genetic programming run the more likely the system is to find a solution. In order to increase the probability of success on difficult problems, designers and users of genetic programming systems often desire access to distributed computation, either locally or across the internet, to evaluate fitness cases more quickly. Most systems for internet-scale distributed computation require a user's explicit participation and the installation of client side software. We present a proof-of-concept system for distributed computation of genetic programming via asynchronous javascript and XML (AJAX) techniques which requires no explicit user interaction and no installation of client side software. Clients automatically and possibly even unknowingly participate in a distributed genetic programming system simply by visiting a webpage, thereby allowing for the solution of genetic programming problems without running a single local fitness evaluation. The system can be easily introduced into existing webpages to exploit unused client-side computation for the solution of genetic programming and other problems.