ALL THINGS BIG AND SMALL 2.0

# Thanks to Victor Venema of the University of Bonn for the image depicting an evolutionary search algorithm Current-day so-called Evolutionary Algorithms (EA) are more about Mendelian selective breeding than about Darwinian evolution. Nature has no preconceived objectives. Evolution is open-ended, and is more about adaptation to the ever-evolving environment than about survival of the fittest. Although the “fittest” can be defined as those that have adapted in the best to the changing environment. Using Evolutionary Algorithms such as Genetic Algorithms to find optimal solutions to decision-making is just directing the algorithm to a static goal.  We cannot model the richness, complexity and dynamism of Nature’s meandering adaptive walks. The road of evolution knows not its final destination. It twists and winds its way; the direction of the next step is the net sum of the interaction (actions and reactions) between all participants at that point in time plu...

One of the most common things we humans face in our daily lives is the problem of optimization. viz what is the optimum solution for a problem using certain mix of resources that we have, and bearing in mind our objective(s) In Life we seldom find that there is only one solution to a problem, but in fact many possible choices for a solution. Here are some examples: Which is the best route to travel from A to B, after defining what is ‘best’. {is it the shortest route in time, which again is different from the shortest route in distance, or is it the most economical route, the most scenic route etc}. What is the minimum risk and maximum gain I can get from re-allocating the number of shares in each stock that I hold in a portfolio? What combination of resources [land, labor, capital] will enable me to complete a certain construction project in a specified period of time. What is the best way to draw up the School timetable using the variables we can juggle with viz the school hours Mon-...