Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
The evolution of size and shape
Advances in genetic programming
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Foundations of genetic programming
Foundations of genetic programming
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Programming and Evolvable Machines
Calculating the expected loss of diversity of selection schemes
Evolutionary Computation
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
General schema theory for genetic programming with subtree-swapping crossover: part I
Evolutionary Computation
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming and Evolvable Machines
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Backward-chaining genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A markov chain framework for the simple genetic algorithm
Evolutionary Computation
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
Schemata evolution and building blocks
Evolutionary Computation
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Balancing Parent and Offspring Selection in Genetic Programming
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A review of tournament selection in genetic programming
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
A gaussian groundplan projection area model for evolving probabilistic classifiers
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Starting from some simple observations on a popular selection method in Evolutionary Algorithms (EAs)-tournament selection-we highlight a previously-unknown source of inefficiency. This leads us to rethink the order in which operations are performed within EAs, and to suggest an algorithm-the EA with efficient macro-selection-that avoids the inefficiencies associated with tournament selection. This algorithm has the same expected behaviour as the standard EA but yields considerable savings in terms of fitness evaluations. Since fitness evaluation typically dominates the resources needed to solve any non-trivial problem, these savings translate into a reduction in computer time. Noting the connection between the algorithm and rule-based systems, we then further modify the order of operations in the EA, effectively turning the evolutionary search into an inference process operating in backward-chaining mode. The resulting backward-chaining EA creates and evaluates individuals recursively, backward from the last generation to the first, using depth-first search and backtracking. It is even more powerful than the EA with efficient macro-selection in that it shares all its benefits, but it also provably finds fitter solutions sooner, i.e., it is a faster algorithm. These algorithms can be applied to any form of population based search, any representation, fitness function, crossover and mutation, provided they use tournament selection. We analyse their behaviour and benefits both theoretically, using Markov chain theory and space/time complexity analysis, and empirically, by performing a variety of experiments with standard and back-ward chaining versions of genetic algorithms and genetic programming.