Competent Program Evolution, Doctoral Dissertation, December 2006

Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability

Estimation of distribution algorithms in logistics : Analysis, design, and application

This thesis considers the analysis, design and application of Estimation of Distribution Algorithms (EDA) in Logistics. It approaches continouos nonlinear optimization problems (standard test problems and stochastic transportation problems) as well as location problems, strategic safety stock placement problems and lotsizing problems. The thesis adds to the existing literature by proposing theoretical advances for continuous EDAs and practical applications of discrete EDAs. Thus, it should be of interest for researchers from evolutionary computation, as well as practitioners that are in need of efficient algorithms for the above mentioned problems

Alayzing The Effects Of Modularity On Search Spaces

We are continuously challenged by ever increasing problem complexity and the need to develop algorithms that can solve complex problems and solve them within a reasonable amount of time. Modularity is thought to reduce problem complexity by decomposing large problems into smaller and less complex subproblems. In practice, introducing modularity into evolutionary algorithm representations appears to improve search performance; however, how and why modularity improves performance is not well understood. In this thesis, we seek to better understand the effects of modularity on search. In particular, what are the effects of module creation on the search space structure and how do these structural changes affect performance? We define a theoretical and empirical framework to study modularity in evolutionary algorithms. Using this framework, we provide evidence of the following. First, not all types of modularity have an effect on search. We can have highly modular spaces that in essence are equivalent to simpler non-modular spaces. This is the case, because these spaces achieve higher degree of modularity without changing the fundamental structure of the search space. Second, for the cases when modularity actually has an effect on the fundamental structure of the search space, if left without guidance, it would only crowd and complicate the space structure resulting in a harder space for most search algorithms. Finally, we have the case when modularity not only has an effect in the search space structure, but most importantly, module creation can be guided by problem domain knowledge. When this knowledge can be used to estimate the value of a module in terms of its contribution toward building the solution, then modularity is extremely effective. It is in this last case that creating high value modules or low value modules has a direct and decisive impact on performance. The results presented in this thesis help to better understand, in a principled way, the effects of modularity on search. Better understanding the effects of modularity on search is a step forward in the larger issue of evolutionary search applied to increasingly complex problems

Improving the efficiency of Bayesian Network Based EDAs and their application in Bioinformatics

Estimation of distribution algorithms (EDAs) is a relatively new trend of stochastic optimizers which have received a lot of attention during last decade. In each generation, EDAs build probabilistic models of promising solutions of an optimization problem to guide the search process. New sets of solutions are obtained by sampling the corresponding probability distributions. Using this approach, EDAs are able to provide the user a set of models that reveals the dependencies between variables of the optimization problems while solving them. In order to solve a complex problem, it is necessary to use a probabilistic model which is able to capture the dependencies. Bayesian networks are usually used for modeling multiple dependencies between variables. Learning Bayesian networks, especially for large problems with high degree of dependencies among their variables is highly computationally expensive which makes it the bottleneck of EDAs. Therefore introducing efficient Bayesian learning algorithms in EDAs seems necessary in order to use them for large problems. In this dissertation, after comparing several Bayesian network learning algorithms, we propose an algorithm, called CMSS-BOA, which uses a recently introduced heuristic called max-min parent children (MMPC) in order to constrain the model search space. This algorithm does not consider a fixed and small upper bound on the order of interaction between variables and is able solve problems with large numbers of variables efficiently. We compare the efficiency of CMSS-BOA with the standard Bayesian network based EDA for solving several benchmark problems and finally we use it to build a predictor for predicting the glycation sites in mammalian proteins

Using Prior Knowledge and Learning from Experience in Estimation of Distribution Algorithms

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. One of the primary advantages of EDAs over many other stochastic optimization techniques is that after each run they leave behind a sequence of probabilistic models describing useful decompositions of the problem. This sequence of models can be seen as a roadmap of how the EDA solves the problem. While this roadmap holds a great deal of information about the problem, until recently this information has largely been ignored. My thesis is that it is possible to exploit this information to speed up problem solving in EDAs in a principled way. The main contribution of this dissertation will be to show that there are multiple ways to exploit this problem-specific knowledge. Most importantly, it can be done in a principled way such that these methods lead to substantial speedups without requiring parameter tuning or hand-inspection of models