Heiner Zille

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In multi-objective optimization, scalable test problems are required to test and compare the search abilities of the algorithms in solving large and small-dimensional problems. In this paper, we analyze a generalized Distance Minimization Problem (DMP) that is scalable in the number of decision variables and objectives and can be used with any distance(More)
In this work we introduce a new method for solving multi-objective optimization problems that involve a large number of decision variables. The proposed <i>Weighted Optimization Framework</i> (WOF) relies on variable grouping and weighting to transform the original optimization problem and is designed as a generic method that can be used with any(More)
This paper proposes a new multi-objective optimization algorithm that is called Fitness-Proportional Attraction with Weights (F-PAW). In contrast to many other approaches, this work was inspired by physics rather than biology. It is based on concepts from several methods, including the attraction principle of gravity from the Gravitational Search Algorithm(More)
In this paper, we study the influence of using variable grouping inside mutation operators for large-scale multi-objective optimization. We introduce three new mutation operators based on the well-known Polynomial Mutation. The variable grouping in these operators is performed using two different grouping mechanisms, including Differential Grouping from the(More)
This paper proposed the method to reduce the calculating time to reveal the functional brain network associated with a task using a genetic algorithm and functional near-infrared spectroscopy (fNIRS) data. Changes in the cerebral blood flow during a task are obtained as time series data is analyzed using fNIRS, and a correlation matrix for multiple fNIRS(More)
In evolutionary swarms adaptability and diversity are closely related concepts. In order to get a better understanding of their codependency we study a heterogeneous evolutionary multi-agent system with different rates of redundancy within the genetic material. The agents process a dynamic multi-objective task, where their genetic material defines their(More)
Scalable multi-objective test problems are known to be useful in testing and analyzing the abilities of algorithms. In this paper we focus on test problems with degenerated Pareto-fronts and provide an in-depth insight into the properties of some problems which show these characteristics. In some of the problems with degenerated fronts such as Distance(More)
In this article we propose a new dynamic multi-objective optimization problem. This dynamic Distance Minimization Problem (dDMP) functions as a benchmark problem for dynamic multi-objective optimization and is based on the static versions from the literature. The dDMP introduces a useful property and challenge for dynamic multi-objective algorithms. Not(More)
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