Architecture and Design of the HeuristicLab Optimization Environment

Abstract

Abstract. 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.

Deconstructing GAs into Visual Software Components

“…Furthermore, although some tools feature visual programming environments (e.g. the operator graph of HeuristicLab [17]) allowing users to connect computational modules, ultimately such programs represent dataflows with no predefined assumptions or guarantees on the intermediate computational steps involved throughout. Rather than a dataflow, the visual assemblage in Goldenberry represent provision and consumption of services precisely specified as contracts that each component is liable to comply, constituting a guarantee of reusability in other EC schemes conforming with said composition specification.…”

An Extensible JCLEC-based Solution for the Implementation of Multi-Objective Evolutionary Algorithms

“…In fact, EvA2 can execute MOGA, NSGA, NSGA-II, PESA, PESA-II, Random Weight GA, SPEA, SPEA2 and VEGA. HeuristicLab (2014) [12]. Developed for the Microsoft .NET environment using C#, this framework looks for the implementation of arbitrary heuristic optimisation algorithms, leading to an architecture based on plugins developed by a community of contributors.…”

Data-Based Identification of Prediction Models for Glucose

“…The maximum model size was set to 50; the population size was set to 100 and the maximum selection pressure was also set to 100. We used the GP implementation in HeuristicLab [14]. For each patient we trained models with minimum time delay 90 minuted and with minimum time delay 120.…”