Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Advances in genetic programming
Advances in genetic programming
Evolution, complexity, entropy and artificial reality
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 1st annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Toward simulated evolution of machine-language iteration
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Genetic algorithms and artificial life
Artificial Life
Crossover accelerates evolution in gas with a babel-like fitness landscape: Mathematical analyses
Evolutionary Computation
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A novel system composed of multiple von Neumann computers and an appropriate problem environment is proposed and simulated. Each computer has a memory to store the machine instruction program, and when a program is executed, a series of machine codes in the memory is sequentially decoded, leading to register operations in the central processing unit (CPU). By means of these operations, the computer not only can handle its generally used registers but also can read and write the environmental database. Simulation is driven by genetic algorithms (GAs) performed on the population of program memories. Mutation and crossover create program diversity in the memory, and selection facilitates the reproduction of appropriate programs. Through these evolutionary operations, advantageous combinations of machine codes are created and fixed in the population one by one, and the higher function, which enables the computer to calculate an appropriate number from the environment, finally emerges in the program memory. In the latter half of the article, the performance of GAs on this system is studied. Under different sets of parameters, the evolutionary speed, which is determined by the time until the domination of the final program, is examined and the conditions for faster evolution are clarified. At an intermediate mutation rate and at an intermediate population size, crossover helps create novel advantageous sets of machine codes and evidently accelerates optimization by GAs.