Principles of artificial intelligence
Principles of artificial intelligence
Tree structured rules in genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Using the genetic algorithm to generate LISP source code to solve the prisoner's dilemma
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Conception, evolution, and application of functional programming languages
ACM Computing Surveys (CSUR)
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming and emergent intelligence
Advances in genetic programming
Scalable learning in genetic programming using automatic function definition
Advances in genetic programming
Alternatives in automatic function definition: a comparison of performance
Advances in genetic programming
Advances in genetic programming
Hierarchical learning with procedural abstraction mechanisms
Hierarchical learning with procedural abstraction mechanisms
Simultaneous evolution of programs and their control structures
Advances in genetic programming
Discovery of subroutines in genetic programming
Advances in genetic programming
Evolving recursive programs for tree search
Advances in genetic programming
Evolving recursive functions for the even-parity problem using genetic programming
Advances in genetic programming
A historical perspective on the evolution of executable structures
Fundamenta Informaticae
Communications of the ACM
Genetic Approaches to Learning Recursive Relations
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Methods to Evolve Legal Phenotypes
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Evolving Modules in Genetic Programming by Subtree Encapsulation
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Grammatical bias for evolutionary learning
Grammatical bias for evolutionary learning
Scaling of program fitness spaces
Evolutionary Computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ACM SIGCOMM Computer Communication Review
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
A self-scaling instruction generator using cartesian genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Invariance of function complexity under primitive recursive functions
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A methodology for user directed search in evolutionary design
Genetic Programming and Evolvable Machines
| Hi-index | 0.00 |
We present a novel approach using higher-order functions and λ abstraction to evolve recursive and modular programs. Moreover, a new term “structure abstraction” is introduced to describe the property emerged from the higher-order function program structure. We test this technique on the general even-parity problem. The results indicate that this approach is very effective with the general even-parity problem due to the appropriate selection of the foldr higher-order function. Initially, foldr structure abstraction identify the promising area of the search space at generation zero. Once the population is within the promising area, foldr structure abstraction provides hierarchical processing for search. Consequently, solutions to the general even-parity problem are found very efficiently. We identify the limitations of this new approach and conclude that only when the appropriate higher-order function is selected that the benefits of structure abstraction show.