JIOP: A Java Intelligent Optimisation And Machine Learning Framework

This paper presents an open source, object-oriented machine learning framework, formally named Java Intelligent Optimisation (JIOP). While JIOP is still in the early stages of development, it already provides a wide variety of general learning algorithms that can be used. Initially designed as a collection of existing learning methods, JIOP aims to emphasise commonalities and dissimilarities of algorithms in order to identify their strengths and weaknesses, providing a simple, coherent and unified view. For this reason, JIOP is suitable for pedagogical purposes, such as for introducing bachelor and master degree students to the concepts of intelligent algorithms. The problems that JIOP aims to solve are initially discussed to demonstrate the need for such a framework. Later on, the design architecture and the current functions of the framework are outlined. As a validating case study, a real application where JIOP is used to minimise the cost function for solving the inverse kinematics (IK) of a KUKA industrial robotic arm with six degrees of freedom (DOF) is also presented. Related simulations are carried out to prove the effectiveness of the proposed framework.

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