A gene regulatory network model for control

The activity of a biological cell is regulated by interactions between genes and proteins. In artificial intelligence, this has led to the creation of developmental gene regulatory network (GRN) models which aim to exploit these mechanisms to algorithmically build complex designs. The emerging field of GRNs for control aims to instead exploit these natural mechanisms and this ability to encode a large variety of behaviours within a single evolvable genetic program for the solution of control problems. This work aims to extend the application domain of GRN models to previously unsolved control problems; the focus will here be on reinforcement learning problems, in which the dynamics of the system controlled are kept from the controller and only sparse feedback is given to it. This category of problems closely matches the challenges faced by natural evolution in generating biological GRNs. Starting with an existing GRN model, the fractal GRN (FGRN) model, a successful application to a standard control problem will be presented, followed by multiple improvements to the FGRN model and its associated genetic algorithm, resulting in better performances in terms of both reliability and speed. Limitations will be identified in the FGRN model, leading to the introduction of the Input-Merge- Regulate-Output (IMRO) architecture for GRN models, an implementation of which will show both quantitative and qualitative improvements over the FGRN model, solving harder control problems. The resulting model also displays useful features which should facilitate further extension and real-world use of the system

ALPS evaluation in financial portfolio optimisation

claims to reduce premature convergence in Evolutionary Al-gorithms. We provide the first evaluation of ALPS on a real-world problem — the evolution of non-linear factor models for financial portfolio optimisation. We incorporate ALPS into our GP system, coupled to an investment simulator, and provide a head-to-head comparison between ALPS GP and Standard GP. By investigating the performance of ALPS both during train-ing and during out-of-sample validation, we provide empirical evidence of the benefits of ALPS; we show that it really does reduce convergence, and provides fitter individuals, in our problem domain. The ALPS GP system evolves non-linear factor models that out-perform not only the Standard GP system, but also the market index by a significant amount. I