Optimization in machine learning

ML facilitates systems to recognize patterns from current available algorithms and datasets and develop feasible solution concepts. In ML algorithms, to recognize the patterns it is required to feed the system with the required algorithms and huge data in advance. Then ML carries out some tasks. First it finds, extracts and summarizes the pertinent data. Then it makes predictions by analyzing the summarized data. After that, it determines probabilities for particular results. Then adapts to assured developments. Finally, optimization of these developments is done based on recognized patterns. The process of obtaining knowledge from available data is called learning process. In the approach of attempts to acquire information from available data, the results are probabilities rather than certainties. So, it is clear that optimization is part of ML. Nature gives a lot of motivation to derive solutions for complex and hard problems of optimization. However, it exhibits extremely dynamic, varied, complex, robust and fascinating occurrences. Nature always discovers the optimal solution, and to find the solutions it preserves perfect stability between its components. This becomes the inspiration for Bio-​inspired optimization algorithms (BIOAs). A number of bio-​inspired optimization methods have been recently designed to solve optimization problems. The next section presents different BIOAs.