Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Another investigation on tournament selection: modelling and visualisation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using Numerical Simplification to Control Bloat in Genetic Programming
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Backward-chaining evolutionary algorithms
Artificial Intelligence
Operator equalisation and bloat free GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
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Artificial Intelligence , volume 170, number 11, pages 953---983, 2006 published a paper titled "Backward-chaining evolutionary algorithm". It introduced two fitness evaluation saving algorithms which are built on top of standard tournament selection. One algorithm is named Efficient Macro-selection Evolutionary Algorithm (EMS-EA) and the other is named Backward-chaining EA (BC-EA). Both algorithms were claimed to be able to provide considerable fitness evaluation savings, and especially BC-EA was claimed to be much efficient for hard and complex problems which require very large populations. This paper provides an evaluation and analysis of the two algorithms in terms of the feasibility and capability of reducing the fitness evaluation cost. The evaluation and analysis results show that BC-EA would be able to provide computational savings in unusual situations where given problems can be solved by an evolutionary algorithm using a very small tournament size, or a large tournament size but a very large population and a very small number of generations. Other than that, the saving capability of BC-EA is the same as EMS-EA. Furthermore, the feasibility of BC-EA is limited because two important assumptions making it work hardly hold.