Evolutionary diversity optimization using multi-objective indicators

Evolutionary diversity optimization aims to compute a set of solutions that are diverse in the search space or instance feature space, and where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neuman

Multi - objective cooperative coevolution of neural networks for time series prediction

The use of neural networks for time series prediction has been an important focus of recent research. Multi-objective optimization techniques have been used for training neural networks for time series prediction. Cooperative coevolution is an evolutionary computation method that decomposes the problem into subcomponents and has shown promising results for training neural networks. This paper presents a multi-objective cooperative coevolutionary method for training neural networks where the training data set is processed to obtain the different objectives for multi-objective evolutionary training of the neural network. We use different time lags as multi-objective criterion. The trained multi-objective neural network can give prediction of the original time series for preprocessed data sets distinguished by their time lags. The proposed method is able to outperform the conventional cooperative coevolutionary methods for training neural networks and also other methods from the literature on benchmark problems

Cooperative coevolution of feed forward neural networks for financial time series problem

Intelligent financial prediction systems guide investors in making good investments. Investors are continuously on the hunt for better financial prediction systems. Neural networks have shown good results in the area of financial prediction. Cooperative coevolution is an evolutionary computation method that decomposes the problem into subcomponents and has shown promising results for training neural networks. This paper presents a computational intelligence framework for financial prediction where cooperative coevolutionary feedforward neural networks are used for predicting closing market prices for companies listed on the NASDAQ stock exchange. Problem decomposition is an important step in cooperative coevolution that affects its performance. Synapse and Neuron level are the main problem decomposition methods in cooperative coevolution. These two methods are used for training neural networks on the given financial prediction problem. The results show that Neuron level problem decomposition gives better performance in general. A prototype of a mobile application is also given for investors that can be used on their Android devices

A Review of Physics Simulators for Robotic Applications

The use of simulators in robotics research is widespread, underpinning the majority of recent advances in the field. There are now more options available to researchers than ever before, however navigating through the plethora of choices in search of the right simulator is often non-trivial. Depending on the field of research and the scenario to be simulated there will often be a range of suitable physics simulators from which it is difficult to ascertain the most relevant one. We have compiled a broad review of physics simulators for use within the major fields of robotics research. More specifically, we navigate through key sub-domains and discuss the features, benefits, applications and use-cases of the different simulators categorised by the respective research communities. Our review provides an extensive index of the leading physics simulators applicable to robotics researchers and aims to assist them in choosing the best simulator for their use case.</p

Automated Design of Heuristics for the Resource Constrained Project Scheduling Problem

Classical project scheduling problem (PSP) usually involves a set of non-preempt-able and precedence related activities that need to be scheduled. The resource constrained project scheduling problem (RCPSP) extends the classical project scheduling by taking into account constraints on the resources required to complete the activities. RCPSP is a widely studied NP~(Non-deterministic Polynomial-time)-Hard optimization problem. One particular approach for solving RCPSP instances is through the use of simple priority heuristics. A priority heuristic can be defined as a function which utilizes certain instance characteristics to construct a solution. Priority heuristics are usually the quickest way of obtaining ``good-enough" solutions and have been useful for a range of different combinatorial optimization problems.Design of priority heuristics, however, is a non-trivial task. Usually, the process involves problem experts who extensively study instance characteristics in order to construct new heuristics. This approach can be time-consuming as well being restrictive in terms of the possibilities that can be considered by an expert. As a result, researchers are increasingly exploring methods to automate construction of heuristics, commonly known as hyper-heuristics. Genetic programming based hyper heuristics (GPHH) are more commonly used for this task. It operates on a set of problem attributes and mathematical operators to evolve heuristics. GPHHs have been used in a number of different domains such as job shop scheduling and routing. The same, however, can not be said about RCPSP, as apart from a few foundational studies, the literature is relatively scant. The work presented in this thesis is directed towards addressing the aforementioned gap in the literature. Firstly, a GPHH is presented for evolving different types (arithmetic and decision-tree) of priority heuristics for RCPSP. The effect of different representations and attributes are empirically evaluated and an attempt is made to evolve priority heuristics which can out-perform existing human designed priority heuristics. Next, a GPHH framework is proposed for evolving variants of the rollout-justification procedure in order to leverage the strength of this approach in discovering heuristics which can perform on par with state-of-the-art algorithmic methods. Finally, a dynamic variant of the classical RCPSP is formulated and a multi-objective GPHH is proposed for discovering priority heuristics, with strong performance and low complexity, to deal with dynamic instances. Apart from these major contributions, other improvements in GP and RCPSP are also proposed as part of the research undertaken during this PhD. The heuristics discovered in this study exhibit, either, on-par or superior performance in comparison to existing human designed approaches