Andreas Scheibenpflug

Architecture and Design of the HeuristicLab Optimization Environment

Many optimization problems cannot be solved by classical mathematical optimization techniques due to their complexity and the size of the solution space. In order to achieve solutions of high quality though, heuristic optimization algorithms are frequently used. These algorithms do not claim to find global optimal solutions, but offer a reasonable tradeoff between runtime and solution quality and are therefore especially suitable for practical applications. In the last decades the success of heuristic optimization techniques in many different problem domains encouraged the development of a broad variety of optimization paradigms which often use natural processes as a source of inspiration (as for example evolutionary algorithms, simulated annealing, or ant colony optimization). For the development and application of heuristic optimization algorithms in science and industry, mature, flexible and usable software systems are required. These systems have to support scientists in the development of new algorithms and should also enable users to apply different optimization methods on specific problems easily. The architecture and design of such heuristic optimization software systems impose many challenges on developers due to the diversity of algorithms and problems as well as the heterogeneous requirements of the different user groups. In this chapter the authors describe the architecture and design of their optimization environment HeuristicLab which aims to provide a comprehensive system for algorithm development, testing, analysis and generally the application of heuristic optimization methods on complex problems.

Optimization knowledge base: an open database for algorithm and problem characteristics and optimization results

This paper describes the optimization knowledge base (OKB), a database for storing information about algorithms and problems. The optimization knowledge base allows to save results of algorithm executions as well as problem-specific information of fitness landscape analyses. This database can be queried and gives researchers a tool for gaining a better understanding of problems and algorithms and their behavior. Therefore the OKB supports parameter tuning and keeping track of tested algorithm and parameter settings as well as their results. Furthermore, the OKB and fitness landscape analysis can be used to not only explain the behavior of algorithms but to calculate similarities between problem instances and algorithms. Based on similarities and already captured knowledge, parameter settings can be extracted that could work well for new problem instances. Additionally, the OKB can be used to publish results of experiments for a broader audience, which advocates transparency of scientific work in the area of metaheuristics.

Knowledge discovery through symbolic regression with heuristiclab

This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification.

Scripting and framework integration in heuristic optimization environments

Rapid prototyping and testing of new ideas has been a major argument for evolutionary computation frameworks. These frameworks facilitate the application of evolutionary computation and allow experimenting with new and modified algorithms and problems by building on existing, well tested code. However, one could argue, that despite the many frameworks of the metaheuristics community, software packages such as MATLAB, GNU Octave, Scilab, or RStudio are quite popular. These software packages however are associated more closely with numerical analysis rather than evolutionary computation. In contrast to typical evolutionary computation frameworks which provide standard implementations of algorithms and problems, these popular frameworks provide a direct programming environment for the user and several helpful functions and mathematical operations. The user does not need to use traditional development tools such as a compiler or linker, but can implement, execute, and visualize his ideas directly within the environment. HeuristicLab has become a popular environment for heuristic optimization over the years, but has not been recognized as a programming environment so far. In this article we will describe new scripting capabilities created in HeuristicLab and give information on technical details of the implementation. Additionally, we show how the programming interface can be used to integrate further metaheuristic optimization frameworks in HeuristicLab. Categories and Subject D.

An Analysis of the Intensification and Diversification Behavior of Different Operators for Genetic Algorithms

Intensification and diversification are two driving forces in genetic algorithms and are frequently the subject of research. While it seemed for decades that a genetic operator can be classified as either the one or the other, it has been shown in the last few years that this assumption is an oversimplified view and most operators exhibit both, diversification and intensification, to some degree. Most papers in this field focus on a certain operator or algorithm configuration as theoretical and generalizable foundations are hard to obtain. In this paper we therefore use a wide range of different configurations and behavior measurements to study the intensification and diversification behavior of genetic algorithms and their operators.

Evolutionary Procedural 2D Map Generation using Novelty Search

This paper presents an evolutionary approach to procedural content generation of 2D maps for computer games. To provide better adaptability to the map designers vision, user preference is incorporated to guide the algorithm. A cooperative method utilizes novelty search as a source of diverse solutions, which are then further optimized by multiple, subsequent genetic algorithms. We compare the results to a second approach based on multi-objective optimization, which takes the two conflicting goals of optimizing towards user preference and finding novel solutions as objective functions to build a Pareto front of maps.

Simplifying Problem Definitions in the HeuristicLab Optimization Environment

Software frameworks for metaheuristic optimization take the burden off researchers and practitioners to start from scratch and implement their own algorithms and problems. One such framework is HeuristicLab. While it allows using existing, already implemented algorithms and problems comfortably and provides an extensive range of tools for analyzing results, it lacks an easy to use programming interface for adding new problems. As implementing new problems is a common task, an improved and simpler problem definition interface has been created. Besides giving an overview of the implementation, we also show examples of problems built using this new interface. Additionally, we compare the new approach to three other metaheuristic frameworks. This is done by analyzing the source code of the OneMax problem implemented in each framework and comparing the resulting lines of code with previous works.

Automatic Adaption of Operator Probabilities in Genetic Algorithms with Offspring Selection

When offspring selection is applied in genetic algorithms, multiple crossover and mutation operators can be easily used together as crossover and mutation results of insufficient quality are discarded in the additional selection step after creating new solutions. Therefore, the a priori choice of appropriate crossover and mutation operators becomes less critical and it even turned out that multiple operators reduce the bias, broaden the search, and thus lead to higher solution quality in the end. However, using crossover and mutation operators which often produce solutions not passing the offspring selection criterion also increases the selection pressure and consequently the number of evaluated solutions.

Optimizing Set-Up Times Using the HeuristicLab Optimization Environment

This publication shows the application of set-up time optimization to machinery that requires some components to be preloaded from a component storage to the work zone before jobs can be processed. Component loading and unloading, which is normally done by the machine operators, should be done automatically. The machine has access to a component storage consisting of multiple racks. Components can be moved by using different strategies. These strategies also affect the storage layout over time. Applying simulation-based optimization to the set-up process yields good machine configuration parameters (i.e. initial storage layout, sequence of jobs and used strategies) for a given job sequence. A simulator which models the machinery is used to evaluate different strategies and machine parameters. For all optimization aspects, HeuristicLab is used as the underlying software environment in combination with a new specific problem type that can be solved with evolutionary algorithms such as genetic algorithms or evolution strategies.

Diversity-Based Offspring Selection Criteria for Genetic Algorithms

Genetic algorithms can be affected by an early loss of diversity in their populations called premature convergence. To address this problem, this paper presents two extensions for the offspring selection genetic algorithm. Both extensions are based on diversity maintenance mechanisms applied when selecting offspring for the next generation. The first approach focuses on producing solutions that feature a predefined quality improvement as well as an appropriate structural distance from their parents. The second approach monitors the average diversity of the population and selects more diverse offspring if the population does not meet a predefined diversity. We show that these algorithms allow to control diversity and are useful methods for influencing the development of the population independent of the algorithms other parameters.