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This work proposes an improved Imperialist Competitive Algorithm (ICA) based algorithm for solving constrained combinatorial problems, called ICA with Independence and Constrained Assimilation (ICAwICA). The proposed algorithm introduces the concept of colony independence -a free will to choose between classic ICA assimilation to the empire's imperialist or any other imperialist in the population. Furthermore, a constrained assimilation process has been implemented that combines classical ICA assimilation and revolution operators, while maintaining population diversity. In order to evaluate the performance and generalisation aspects of the proposed approach, two different kinds of combinatorial benchmark problems were selectedsubset selection and routing, Multiple Knapsack Problem (MKP) and Multiple Depot Vehicle Routing Problem (MDVRP), respectively. The algorithm showed definite improvement over classic ICA and outperformed most of the competition on both types of problems across multiple instances, indicating the generic, universal nature of the ICAwICA. Moreover, it ranked 2 nd among the recently published algorithms that are customised to the specific problem with the use of problem-specific operators, while the proposed algorithm had no such operators.

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Since the introduction of betting exchanges in 2000, there has been increased interest of ways to monetize on the new technology. Betting exchange markets are fairly similar to the financial markets in terms of their operation. Due to the lower market share and newer technology, there are very few tools available for automated trading for betting exchanges. The in-depth analysis of features available in commercial software demonstrates that there is no commercial software that natively supports machine learned strategy development. Furthermore, previously published academic software products are not publicly obtainable. Hence, this work concentrates on developing a full-stack solution from data capture, back-testing to automated Strategy Agent development for betting exchanges. Moreover, work also explores ways to forecast price movements within betting exchange using new machine learned trading strategies based on Artificial Neuron Networks (ANN) and Cartesian Genetic Programming (CGP). Automatically generated strategies can then be deployed on a server and require no human interaction. Data explored in this work were captured from 1st of January 2016 to 17th of May 2016 for all GB WIN Horse Racing markets (total of 204GB of data processing). Best found Strategy agent shows promising 83% Return on Investment (ROI) during simulated historical validation period of one month (15th of April 2016 to 16th of May 2016).

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