Huayang Xie

An analysis of constructive crossover and selection pressure in genetic programming

A common problem in genetic programming search algorithms is destructive crossover in which the offspring of good parents generally has worse performance than the parents. Designing constructive crossover operators and integrating some local search techniques into the breeding process have been suggested as solutions. This paper reports on experiments demonstrating that premature convergence may happen more often when using these techniques in combination with standard parent selection. It shows that modifying the selection pressure in the parent selection process is necessary to obtain a significant performance improvement.

Genetic programming for automatic stress detection in spoken english

This paper describes an approach to the use of genetic programming (GP) for the automatic detection of rhythmic stress in spoken New Zealand English. A linear-structured GP system uses speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. Error rate is used as the fitness function. In addition to the standard four arithmetic operators, this approach also uses several other arithmetic, trigonometric, and conditional functions in the function set. The approach is evaluated on 60 female adult utterances with 703 vowels and a maximum accuracy of 92.61% is achieved. The approach is compared with decision trees (DT) and support vector machines (SVM). The results suggest that, on our data set, GP outperforms DT and SVM for stress detection, and GP has stronger automatic feature selection capability than DT and SVM.

Another investigation on tournament selection: modelling and visualisation

Tournament selection has been widely used and studied in evolutionary algorithms. To supplement the study of tournament selection, this paper provides several models describing the probabilities that a program of a particular rank is sampled and is selected in the standard tournament selection in a simple situation and a complex situation. This paper discovers that, with the same tournament size, trends of sampling probability of a program and selection probability distributions of a population are the same regardless ofthe population size. This paper also models and investigates an alternative tournament selection method which eliminates one of the drawbacks in the standard tournament selection. Finally, this paper proposes a new fitness evaluation saving algorithm via the use of not-sampled individuals, which is a special property of tournament selection.

Automatic Selection Pressure Control in Genetic Programming

Selection pressure must be dynamically managed in response to the changing evolutionary process in order to improve the effectiveness and efficiency of genetic programming (GP) systems using tournament selection. Instead of changing the tournament size and/or the population size via an arbitrary function to influence the selection pressure, this paper focuses on designing an automatic selection pressure control approach. In our approach, populations are clustered based on a dynamic program property. Then clusters become tournament candidates. The selection pressure in the tournament selection method is automatically changed during evolution according to the dynamically changing number of tournament candidates. Our approach is compared with the standard GP system (with no selection pressure control) on two problems with different kinds of fitness distributions. The results show that the automatic selection pressure control approach can improve the effectiveness and efficiency of GP systems

Is the not-sampled issue in tournament selection critical?

The standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have some individuals not sampled at all during the selection phase. The not-sampled issue aggravates the loss of program diversity. However, it is not clear how the issue affects genetic programming (GP) search. This paper investigates the importance of the issue. The theoretical and experimental results show that the issue can be solved and the loss of diversity contributed by not-sampled individuals can be minimised. However, doing so does not appears to significantly improve a GP system. Our conclusion is that the not-sampled issue does not seriously affect the selection performance in the standard tournament selection.

Genetic Programming for detecting rhythmic stress in spoken English

Rhythmic stress detection is an important but difficult problem in speech recognition. This paper describes an approach to the automatic detection of rhythmic stress in New Zealand spoken English using a linear genetic programming system with speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. In addition to the four standard arithmetic operators, this approach also uses other functions such as trigonometric and conditional functions in the function set to cope with the complexity of the task. The error rate on the training set is used as the fitness function. The approach is examined and compared to a decision tree approach and a support vector machine approach on a speech data set with 703 vowels segmented from 60 female adult utterances. The genetic programming approach achieved a maximum average accuracy of 92.6%. The results suggest that the genetic programming approach developed in this paper outperforms the decision tree approach and the support vector machine approach for stress detection on this data set in terms of the detection accuracy, the ability of handling redundant features, and the automatic feature selection capability.

Diversity control in GP with ADF for regression tasks

This paper proposes a two-phase diversity control approach to prevent the common problem of the loss of diversity in Genetic Programming with Automatically Defined Functions. While most recent work focuses on diagnosing and remedying the loss of diversity, this approach aims to prevent the loss of diversity in the early stage through a refined diversity control method and a fully covered tournament selection method. The results on regression tasks suggest that these methods can effectively improve the system performance by reducing the incidences of premature convergence and the number of generations needed for finding an optimal solution.

An analysis of multi-sampled issue and no-replacement tournament selection

Standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have the same individual sampled multiple times in a tournament. Although the impact of this multi-sampled issue on genetic programming is not clear, some researchers believe that it may lower the probability of some good individuals being sampled or selected. One solution is to use an alternative tournament selection (no-replacement tournament selection), which samples individuals in a tournament without replacement. This paper analyses no-replacement tournament selection to investigate the impact of the scheme and the importance of the issue. Theoretical simulations show that when common tournament sizes and population sizes are used, no-replacement tournament selection does not make the selection behaviour significantly different from that in the standard one and that the multi-sampled issue seldom occurs. In general, the issue is not crucial to the selection behaviour of standard tournament selection.

Genetic Programming for New Zealand CPI Inflation Prediction

Reserve Bank of New Zealand (RBNZ) is one of many inflation-targeting central banks. The effective conduct of monetary policy requires the capacity to make accurate short and medium term predictions about price inflation. The RBNZs prediction system is very complex, requiring many iterations and significant input from human experts. This paper investigates the capability of Genetic Programming (GP) to predict price inflation over short and medium terms. By using un-preprocessed economic time series over small intervals, the experimental results demonstrate that GP can produce predictions of price inflation with accuracy comparable to the RBNZs official prediction system, over both short and medium terms.

A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming

Good predecessor programs (GPPs) are the ancestors of the best program found in a genetic programming (GP) evolution. This paper reports on an investigation into GPPs with the ultimate goal of reducing fitness evaluation cost in tree-based GP systems. A framework is developed for gathering information about GPPs and a series of experiments is conducted on a symbolic regression problem, a binary classification problem, and a multi-class classification program with increasing levels of difficulty in different domains. The analysis of the data shows that during evolution, GPPs typically constitute less than 33% of the total programs evaluated, and may constitute less than 5%. The analysis results further shows that in all evaluated programs, the proportion of GPPs is reduced by increasing tournament size and to a less extent, affected by population size. Problem difficulty seems to have no clear influence on the proportion of GPPs.