Artificial Life

People make machines to do things more rapidly and  productively, yet some  people accept that our own developments may one day become our own kids. Machines are developed to minimize work and manpower.

Are machines fit to be considerd as living creatures?

Artificial Life is more interesting because biologists started to believe that life itself is a great teacher. It never stops teaching you.

ALife ( Artificial Life) was instituted by Christopher Langton in the 1980’s. Artificial Life is a field of study to describe research on human made systems that has some crucial properties of real life. Artificial Life follows a life like process. With the help of nature, many human made inventions are available in today’s market. The main goal of artificial life is to develop practical devices based on the behaviour of nature.

A-Life includes two kinds of researches:

• How computational methods can help to study biological phenomena?
• How natural strategies can assist with computational issue?

Real time applications of artificial life

1. Computer gaming
2. Optimization algorithms
3. Computer Animations
4. Designing problem

Towards Technology through Nature

1. The kingfisher’s nose turns into the model for the nose cone of Japan’s 500 series shinkansen bullet train.

2. Mercedes-Benz bionic vehicle at ‘The Museum of Modern Art in New York’: It’s design and innovation is motivated by fish’s body shape.

3. Nature acts as a motivation for some structures and developments. One of the primary advancements is the structure of the plane  that has followed the shape of the bird .

When you really pay attention…!Everything is your teacher !!!

Nature always provides some of the best ways to solve our real life problems.

Some of the important properties of nature

1. Supports self optimization

2. Supports self healing

3. Supports self Learning

4. Supports self processing

5. Supports self organizing

Optimization plays an important role in these studies.

Optimization and its spirits

Optimization is extracted from the Latin word “optimus” , which means the best. Optimization is the process of finding the best solution from all feasible options.

• Nature gives many best ways to solve our problems.
• It is about surviving with what one has and getting the best out of it.

Optimization is a way of life !

Optimization strategies help  in business and public administrations to maximize the throughput and minimize the cost. These two parameters play a vital role in all leading industries. To achieve these types of optimization in business, we need to set some constraints to solve the problem.

Real life examples of optimization problem

• Train ticket scheduling
• Traveling salesman problem
• Circuit Design

Some nature Inspired optimization techniques

1. Evolutionary algorithm
2. Ant colony optimization
3. Spider monkeys optimization
4. Artificial immune system
5. Bee colony optimization

Evolutionary algorithm

Evolutionary algorithm is based on the advancement of natural species.” Charles Darwin” proposed the ‘Evolution theory’ in the year 1859. Natural organism changes their behavioral traits over time.

It is not the strongest of the species that survive, nor the most intelligent, but the ones most responsive to change!!!

Artificial immune system

Artificial immune system is inspired from the immune system of human body.In the regular habitat, various types of pathogens, for example, unsafe germs and infections, enter into our bodies, which leads to death. We have a powerful immune system to solve such kind of problems.

Bee colony optimization

It is one kind of optimization technique inspired from the foraging nature of the honey bees. Honey bees perform one special dance called waggle dance. One bee is performing the waggle dance and the remaining bees are closely watching the movement. It is used to identify the location of the food source and the direction away from the nest.

Spider monkeys optimization 

Spider monkeys optimization algorithm is a sub field of swarm optimisation algorithm.

Swarm optimization works based on the Foraging behaviour of the birds, frogs, etc.. These animals follow the fission- fusion structure. It dynamically changes the structure based on the environment.

• Fission- fusion structured animals always live in groups ( 40 to 50 members).
• To minimize the foraging competition, it divides the group into smaller sub groups. Each sub group has one leader ( local leader).
• Now, the local leaders take the responsibility to search food.
• If the leader is not able to collect  enough food for their group, the local leaders decide to divide the group again.
• The group individuals convey among themselves and with other sub group individuals, to keep up social bonds and regional limits

Great things in business are never done by one person!  They are done by a team of people!!!

Nature is the best therapist

Ant colony optimization

Ant colony optimization algorithm is also used to find the optimal path based on the foraging behaviour of ants. Ants have one special behaviour to Search food.

Ant always moves in and around the colony for their food. Once the food is found, it will intimate it to others. It produces one organic compound called pheromone based on the capacity of the pheromone other ants find their optimal path.

Conclusion

ALife(Artificial Life) is an interdisciplinary science including brain research, psychological science, arithmetic, science and complex calculations. Improvement of models in synthetic science with suitable approval finds huge application in biomedicine. In its present status, normalization of incorporated natural parts and formalization of procedures is a challenge.