Nature inspired computing, strives to develop new computing techniques through observing how naturally occurring phenomena behave to solve complex problems in various environmental situations. Some activities inspired by nature include ant colony, particle swarm, whales, genetics, etc. One of the most popular architectures Neural network is inspired by human brain activity using neuron signals. Hence these help us solve multiple real word problems with minimum computation.
When we try to solve a selection problem containing multiple items is highly difficult by brut force. Hence, we use an evolutionary algorithm to solve this challenging task. In our problem, we need to select the best possible combination of bags to be filled in a security van that has a weight limit. To solve this we evolve the initial random population and the steps are as follows.
- Generate population
- Tournament selection
- Crossover
- Mutation
- Fitness function These steps for a given set of iterations will result in solutions optimal to solve the issue.
The main focus is to solve a Travelling thief problem which is the combination of the travelling salesman and knapsack problem. The objective of the problem is to find the optimized routes and profits by taking into account the constraints of knapsack weights and the renting ratio. The first step is to find the best possible paths after multiple generations using a genetic algorithm for the given data. Next is to use the best path obtained to pick the best combinations of items using the genetic algorithm.