Challenges of Bioinspired Artificial Intelligence with Genetic Algorithms
Authors: Wilmer Pereira
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At first, basic definitions are presented to understand the scope of Artificial Intelligence, highlighting their positions in order to, at least, simulate human reasoning in a computer. Specifically, we identify the efforts to define formal systems that can model mathematics and therefore human reasoning under the assumption that human intelligence works under a mechanistic paradigm. However, there are insurmountable limitations in all formal systems, so we must settle for strategies that converge to sub-optimal solutions. A stream of Artificial Intelligence establishes this search through techniques inspired by biology, which go beyond the intrinsic limits of deductive mathematical reasoning, since they have proven their efficacy. Consequently, we present the basic principles that govern evolution and genetics,and, the dynamics that underlie genetic algorithms in general. Finally, we will show some results that we have published, using in particular monobjective and multiobjective genetic algorithms, to solve problems on which, in principle, formal operations research techniques or formal systems in general can not be applied.