Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Incorporating A-Priori Expert Knowledge in Genetic Algorithms
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Exploiting expert knowledge in genetic programming for genome-wide genetic analysis
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
A GPU-based implementation of an enhanced GEP algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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We investigated several methods for utilizing expert knowledge in evolutionary search, and compared their impact on performance and scalability into increasingly complex problems. We collected data over one thousand randomly generated problems. We then simulated collecting expert knowledge for each problem by optimizing an approximated version of the exact solution. We then compared six different methods of seeding the approximate model in to the genetic program, such as using the entire approximate model at once or breaking it into pieces. Contrary to common intuition, we found that inserting the complete expert solution into the population is not the best way to utilize that information; using parts of that solution is often more effective. Additionally, we found that each method scaled differently based on the complexity and accuracy of the approximate solution. Inserting randomized pieces of the approximate solution into the population scaled the best into high complexity problems and was the most invariant to the accuracy of the approximate solution. Furthermore, this method produced the least bloated solutions of all methods. In general, methods that used randomized parameter coefficients scaled best with the approximate error, and methods that inserted entire approximate solutions scaled worst with the problem complexity.