Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

Evolving Static Representations for Task Transfer

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Previous approaches to transfer in Keepaway have focused on transforming the original representation to fit the new task. In contrast, this paper explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To demonstrate this point, a bird\u27s eye view (BEV) representation is introduced that can represent different tasks on the same two-dimensional map. For example, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV. Yet the problem is that a raw two-dimensional map is high-dimensional and unstructured. This paper shows how this problem is addressed naturally by an idea from evolutionary computation called indirect encoding, which compresses the representation by exploiting its geometry. The result is that the BEV learns a Keepaway policy that transfers without further learning or manipulation. It also facilitates transferring knowledge learned in a different domain, Knight Joust, into Keepaway. Finally, the indirect encoding of the BEV means that its geometry can be changed without altering the solution. Thus static representations facilitate several kinds of transfer

Improving reinforcement learning function approximators via neuroevolution

Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic program-ming and statistical sampling to estimate the long-term value of taking each action in each state. In most problems of real-world interest, learning this value function requires a func-tion approximator, which represents the mapping from state-action pairs to values via a concise, parameterized function and uses supervised learning methods to set its parameters. Function approximators make it possible to use temporal dif-ference methods on large problems but, in practice, the fea-sibility of doing so depends on the ability of the human de-signer to select an appropriate representation for the value function. My thesis presents a new approach to function approximation that automates some of these difficult desig

Effective Task Transfer Through Indirect Encoding

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird’s eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation. Yet a challenge for such representation is that a raw two-dimensional map is highdimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on iii modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain