Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming
Genetic Programming and Evolvable Machines
The Advantages of Landscape Neutrality in Digital Circuit Evolution
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Seeding Genetic Programming Populations
Proceedings of the European Conference on Genetic Programming
Towards the Automatic Design of More Efficient Digital Circuits
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Finding needles in haystacks is harder with neutrality
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Deceptiveness and neutrality the ND family of fitness landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An empirical investigation of how and why neutrality affects evolutionary search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Co-evolution of active sensing and locomotion gaits of simulated snake-like robot
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Neutral variations cause bloat in linear GP
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Bloat control operators and diversity in genetic programming: A comparative study
Evolutionary Computation
IEEE Transactions on Robotics
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The parsimony control in genetic programming (GP) is one of the limiting factors in the quick evolution of efficient solutions. A variety of parsimony pressure methods have been developed to address this issue. The effects of these methods on the efficiency of evolution are recognized to depend on the characteristics of the applied problem domain. On the other hand, the implications of using parsimony pressure in evolving the seeds for incremental genetic programming (IGP) are still poorly known and remain uninvestigated. In this work we present a study on the cumulative effect of the bloat and the seeding of the initial population on the efficiency of incremental evolution of simulated snake-like robot (Snakebot). In the proposed IGP, the task of coevolving the locomotion gaits and sensing of the bot in a challenging environment is decomposed into two sub-tasks, implemented as two consecutive evolutionary stages. First, to evolve the pools of sensorless Snakebots, we use GP featuring the following three bloat-control methods: (1) linear parametric parsimony pressure, (2) lexicographic parsimony pressure and (3) no bloat control. During the second stage of IGP, we use these pools to seed the initial population of Snakebots applying two methods of seeding: canonical seeding and seeding inspired by genetic transposition (GT).