Genetic programming (GP) is applied to a multobjective optimisation problem and the advantages of its hierarchical tree encoding scheme are compared with an earlier use of a subset representation approach which used string-encoded genetic algorithms. The GP approach is applied to the identification of non-linear system polynomial models and provides a trade-off between the complexity and the pe...

The aim of this chapter is to investigate whether Genetic Programming (GP) can be used as a nature inspired human competitive solution for producing machine. A primary goal of machine learning strategies is to allow the user to state a problem in as high a level as possible and provide a solution without further human intervention. While we are still some way off this goal, GP has proven itself...

Speciication reenement is part of formal program derivation, a method by which software is directly constructed from a provably correct spe-ciication. Because program derivation is an intensive manual exercise used for critical software systems, an automated approach would allow it to be viable for many other types of software systems. The goal of this research is to determine if genetic progra...

Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastr...

the problem of staff scheduling at a truck hub for loading and stripping of the trucks is an important and difficult problem to optimize the labor efficiency and cost. the trucks enter the hub at different hours a day, in different known time schedules and operating hours. in this paper, we propose a goal programming to maximize the labor efficiency via minimizing the allocation cost. the propo...

We describe the programming system for the graph-transformation language GP, focusing on the implementation of its compiler and abstract machine. We also compare the system’s performance with other graph-transformation systems. The GP language is based on conditional rule schemata and comes with a simple formal semantics which maps input graphs to sets of output graphs. The implementation faith...

In this paper a fuzzy goal programming technique is presented to solve multiobjective decision making problems in a probabilistic decision making environment where the right sided parameters associated with the system constraints are exponentially distributed fuzzy random variables. In model formulation of the problem, the fuzzy chance constrained programming problem is converted into a fuzzy p...

Neural networks with step activation function can be very efficient ways of performing non linear mappings. However, no standard learning algorithm exists for training this kind of neural networks. In this work we use Genetic Programming (GP) to discover supervised learning algorithms which can train neural networks with step activation function. Thanks to GP, a new learning algorithm has been ...

In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propaga...

The relationship between generalization and solutions functional complexity in genetic programming (GP) has been recently investigated. Three main contributions are contained in this paper: (1) a new measure of functional complexity for GP solutions, called Graph Based Complexity (GBC) is defined and we show that it has a higher correlation with GP performance on out-of-sample data than another...