The Push programming language was developed for use in genetic and evolutionary computation systems, as the representation within which evolving programs are expressed. It has been used in the production of several significant results, including results that were awarded a gold medal in the Human Competitive Results competition at GECCO-2004. One of Push’s attractive features in this context is its transparent support for the expression and evolution of modular architectures and complex control structures, achieved through explicit code self-manipulation. The latest version of Push, Push3, enhances this feature by permitting explicit manipulation of an execution stack that contains the expressions that are queued for execution in the interpreter. This paper provides a brief introduction to Push and to execution stack manipulation in Push3. It then presents a series of examples in which Push3 was used with a simple genetic programming system (PushGP) to evolve programs with non-trivial control structures.

We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from the training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to significant convergence speedup and, more importantly, significantly reduces the risk of overfitting.

We consider multitask learning of visual concepts within genetic programming (GP) framework. The proposed method evolves a population of GP individuals, with each of them composed of several GP trees that process visual primitives derived from input images. The two main trees are delegated to solving two different visual tasks and are allowed to share knowledge with each other by calling the remaining GP trees (subfunctions) included in the same individual. The method is applied to the visual learning task of recognizing simple shapes, using generative approach based on visual primitives, introduced in [17]. We compare this approach to a reference method devoid of knowledge sharing, and conclude that in the worst case cross-task learning performs equally well, and in many cases it leads to significant performance improvements in one or both solved tasks.

This paper presents a parameter study of our Compressed Linear Genetic Programming (cl-GP) using the Even-n-parity problem. A cl-GP system is a linear genetic programming (GP) which uses substring compression as a modularization scheme. Despite the fact that the compression of substrings assumes a tight linkage between alleles, this approach improves the search process. The compression of the genotype, which is a form of linkage learning, provides both a protection mechanism and a form of genetic code reuse. This text presents a study of the different parameters of the cl-GP on Even-n-parity. Experiments indicate that the cl-GP performs best when compressing a small fraction of the population and the length of the substituted substrings is rather short.

Crucial to the more widespread use of evolutionary computation techniques is the ability to scale up to handle complex problems. In the field of genetic programming, a number of decomposition and reuse techniques have been devised to address this. As an alternative to the more commonly employed encapsulation methods, we propose an approach based on the division of test input cases into subsets, each dealt with by an independently evolved code segment. Two program architectures are suggested for this hierarchical approach, and experimentation demonstrates that they offer substantial performance improvements over more established methods. Difficult problems such as even-10 parity are readily solved with small population sizes.

Abstract. We propose a learning algorithm that reuses knowledge acquired in past learning sessions to improve its performance on a new learning task. The method concerns visual learning and uses genetic programming to represent hypotheses, each of them being a procedure that processes visual primitives derived from the training images. The process of recognition is generative, i.e., a procedure is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This basic method is extended with a knowledge reuse mechanism that allows learners to import genetic material from hypotheses that evolved for the other decision classes (object classes). We compare both methods on a task of handwritten character recognition, and conclude that knowledge reuse leads to signi cant improvement of classi cation accuracy and reduces the risk of over tting. 1

We propose a method of knowledge reuse between evolutionary processes that solve different optimization tasks. We define the method in the framework of tree-based genetic programming (GP) and implement it as code reuse between GP trees that evolve in parallel in separate populations delegated to particular tasks. The technical means of code reuse is a crossbreeding operator which works very similar to standard tree-swapping crossover. We consider two variants of this operator, which differ in the way they handle the incompatibility of terminals between the considered problems. In the experimental part we demonstrate that such code reuse is usually beneficial and leads to success rate improvements when solving the common boolean benchmarks.

Abstract. We propose a method of knowledge reuse for an ensemble of genetic programming-based learners solving a visual learning task. First, we introduce a visual learning method that uses genetic programming individuals to represent hypotheses. Individuals-hypotheses process image representation composed of visual primitives derived from given training images that contain objects to be recognized. The process of recognition is generative, i.e., an individual is supposed to restore the shape of the processed object by drawing its reproduction on a separate canvas. This canonical method is in the following extended with a knowledge reuse mechanism that allows a learner to import genetic material from hypotheses that evolved for other decision classes (object classes). We compare the performance of the extended approach to the basic method on a real-world tasks of handwritten character recognition, and conclude that knowledge reuse leads to signi cant convergence speedup and reduces the risk of over tting. 1

Compressed Linear Genetic Programming (cl-GP) uses substring compression as a modularization scheme. Despite the fact that the compression of substrings assumes a tight linkage between alleles, this approach improves the GP search process. The compression of the genotype, which is a form of linkage learning, provides both a protection mechanism and a form of genetic code reuse. This text presents the results obtained with the cl-GP on the Even-n-parity problem. Results indicate that the modularization of the cl-GP performs better than a normal l-GP as it allows the cl-GP to preserve useful gene combinations. Additionally the cl-GP modularization is well suited for problems where the problem size is adjusted in a co-evolutionary setup, the problem size increases each time a solution is found.

This paper presents a modularization strategy for linear genetic programming (GP) based on a substring compression/substitution scheme. The purpose of this substitution scheme is to protect building blocks and is in other words a form of learning linkage. The compression of the genotype provides both a protection mechanism and a form of genetic code reuse. This paper presents results for synthetic genetic algorithm (GA) reference problems like SEQ and OneMax as well as several standard GP problems. These include a real world application of GP to data compression. Results show that despite the fact that the compression substrings assumes a tight linkage between alleles, this approach improves the search process.