Representation Of Cartesian Genetic Programming Research Articles

Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression

The exploration---exploitation trade-off is an important aspect of evolutionary algorithms which determines the efficiency and accuracy of these algorithms. Cartesian Genetic Programming (CGP) is a generalization of the graph based genetic programming. It is implemented with mutation only and does not have any possibility to share information among solutions. The main goal of this paper is to present an effective method for balancing the exploration and exploitation of CGP referred to as Balanced Cartesian Genetic Programming (BCGP) by incorporating distinctive features from biogeography-based optimization (BBO) and opposition-based learning. To achieve this goal, we apply BBO's migration operator without considering any modifications in the representation of CGP. This operator has good exploitation ability and can be used to share information among individuals in CGP. In addition, in order to improve the exploration ability of CGP, a new mutation operator is integrated into CGP inspired from the concept of opposition-based learning. Experiments have been conducted on symbolic regression. The experimental results show that the proposed BCGP method outperforms the traditional CGP in terms of accuracy and the convergence speed.

Cartesian Genetic Programming and its Application to Medical Diagnosis

Cartesian Genetic Programming (CGP) is a form of genetic programming that is flexible and adaptable to a range of problems. In this article, a particular representation of CGP, known as implicit context representation CGP is presented and its application to two medical conditions: the diagnosis of Parkinson' disease and the detection of breast cancer from mammograms. CGP has a number of advantages over conventional genetic programming and is well suited to the highly non-linear problems considered here. Summary results are presented for the application of CGP to real patient data that are sufficiently encouraging to warrant further clinical trials which are currently in progress.

Design Electronic Circuits Using Evolutionary Algorithms

For the evolutionary algorithm, the representation of the electronic circuit has two methods, one kind is code with the electronic circuit solution space, the other is code with the problem space. How one representation quality weighs may think the following questions? The first is the code method should as far as possible complete, it is say for the significance solution circuit or the optimize solution obtains in the problem space may represented by this code method. The second is the code method should speeds up the convergence speed of the algorithm search. The hardware representation methods mainly include binary bit string representation, tree representation, Cartesian Genetic Programming representation and other representations. In this paper, we introduce the representations of the binary bit string and Cartesian Genetic Programming in detail, and based on the two representations we design the evolutionary algorithm for electronic circuits optimization. Then we introduce the encoding of the circuit as a chromosome, the genetic operators and the fitness function of our algorithm. For the case studies showed this algorithm has proved to be efficient, and the experiment results showed that we have gained better results.