Run time libraries (RTL) in genetic programming (GP) represent a scenario in which individuals evolved under an earlier independent evolutionary run can be potentially incorporated into a following GP run. To date, schemes for exploiting the RTL metaphor have emphasized syntactic over behavioural approaches. Thus, instructions are added to the later run such that the previous code can be explicitly indexed. In this work we demonstrate how the RTL concept is naturally supported by adopting a symbiotic framework for coevolution. Under the Pinball reinforcement learning task, we demonstrate how the initial RTL can be coevolved within a simpler formulation of the task and then used as the basis for providing solutions to a more difficult target task under the same domain. The resulting solutions are stronger than an RTL as coevolved against the target task alone or symbiosis as evolved without support for RTL.

Adopting a symbiotic model of evolution separates context for deploying an action from the action itself. Such a separation provides a mechanism for task decomposition in temporal sequence learning. Moreover, previously learned policies are taken to be synonymous with meta actions (actions that are themselves policies). Should solutions to the task not be forthcoming in an initial round of evolution, then solutions from the earlier round represent the 'meta' actions for a new round of evolution. This provides the basis for evolving policy trees. A benchmarking study is performed using the Acrobot handstand task. Solutions to date from reinforcement learning have not been able to approach the performance of those established 14 years ago using an A* search and a priori knowledge regarding the Acrobot energy equations. The proposed symbiotic approach is able to match and, for the first time, better these results. Moreover, unlike previous work, solutions are tested under a broad range of Acrobot initial conditions, with hierarchical solutions providing significantly better generalization performance.

Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem domains.

Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a non-overlapping behavioral trait, from a single population. The Symbiotic Bid-Based (SBB) algorithm is demonstrated to fit this purpose under an evaluation utilizing data sets with 650 to 5,000 attributes. The resulting solutions are one to two orders simpler than solutions identified under the alternative embedded paradigms of C4.5 and MaxEnt.

Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team members. This is in contrast to existing teaming approaches where individuals learn a direct input-output map (e.g., from exemplars to class labels), allowing the approach to scale to problems with multiple outcomes (classes), while at the same time providing a mechanism for choosing an outcome from those suggested by team members. This paper proposes a symbiotic relationship that continues to support the cooperative bid-based process for problem decomposition while making the credit assignment process much clearer. Specifically, team membership is defined by a team population indexing combinations of GP individuals in a separate team member population. A Pareto-based competitive coevolutionary component enables the approach to scale to large problems by evolving informative test points in a third population. The ensuing Symbiotic Bid-Based (SBB) model is evaluated on three large classification problems and compared to the XCS learning classifier system (LCS) formulation and to the support vector machine (SVM) implementation LIBSVM. On two of the three problems investigated the overall accuracy of the SBB classifiers was found to be competitive with the XCS and SVM results. At the same time, on all problems, the SBB classifiers were able to detect instances of all classes whereas the XCS and SVM models often ignored exemplars of minor classes. Moreover, this was achieved with a level of model complexity significantly lower than that identified by the SVM and XCS solutions.

A bid-based approach for coevolving Genetic Programming classifiers is presented. The approach coevolves a population of learners thatdecompose the instance space by way of their aggregate bidding behaviour. To reduce computation overhead, a small, relevant, subsetof training exemplars is (competitively) coevolved alongside the learners. The approach solves multi-class problems using a single population and is evaluated on three large datasets. It is found tobe competitive, especially compared to classifier systems, whilesignificantly reducing the computation overhead associated withtraining.

The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block DSS algorithm, where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30000-500000 training exemplars demonstrates that both the cascade and balanced block algorithms are able to reduce the likelihood of degenerates while providing a significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day.

A cascaded model is introduced for mining large datasets using genetic programming without recourse to specialist hardware. Such an algorithm satisfies the seeming conflicting requirements of scalability and accuracy on large datasets by incrementally building GP classifiers through the use of a hierarchical dynamic subset selection algorithm. Models are built incrementally with each layer of the cascade receiving as input the original feature vector, plus the output from the previous layer(s). In order to encourage each layer to explicitly solve new aspects of the problem a combination of sum square error and niching is utilized. Thus, previous layers of the model are considered a niche, and the cost function is a shared error metric.

Fixed-length genetic programming is applied to the problem of promoter identification in eukaryotes. The goal is to generate solutions that can be easily interpreted and compared with known promoter characteristics. Using a Boolean function set applied to Boolean registers, inputs, and constant values, the approach builds a logical expression whose value gives the classification decision. Evaluated on a dataset of human promoters and non-promoters from coding regions, the approach is found to generate concise solutions that yield good specificity but poor sensitivity. Analysis of the programs that are generated indicates that a well-known, biologically significant, characteristic of promoter regions is successfully identified. Suggested future work involves implementing the system using fuzzy logic.

The cascade architecture for incremental learning is demonstrated within the context of genetic programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing platforms is retained using the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar 'difficulty' and 'age'. Finally, the ensuing empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.