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Special Issue on Hybrid Learning Machines

The concept of Machine Intelligence (MI) is complex, and thus many theories and definitions have emerged recently. Last few decades have seen a new era of machine intelligence focusing on the principles, theoretical aspects, and design methodology of algorithms gleaned from nature and biology. Examples are artificial neural networks inspired by mammalian neural systems, evolutionary computation inspired by natural selection in biology, simulated annealing inspired by thermodynamics principles and swarm intelligence inspired by collective behavior of insects or micro-organisms, etc. interacting locally with their environment causing coherent functional global patterns to emerge. These techniques have found their way in solving some real world problems in science, business, technology and commerce. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Despite the advances made, progress across the board has been moderate. One reason stems from the relatively slow pace at which work to understand biological intelligence has progressed. Another reason is rooted in the same inertia that has hampered the development of the research on intelligence previously – the reluctance to actively benefit from the achievements of the hybrid approaches.