11.Bio Inspired Approach as a Problem Solving Technique.pdf

Network and Complex Systems www.iiste.org
ISSN 2224-610X (Paper) ISSN 2225-0603 (Online)
Vol 2, No.2, 2012
14
Bio Inspired Approach as a Problem Solving Technique
Harini Chakravarthy*
Pomil Bachan Proch Roshini Rajan K. Chandrasekharan
Department of Computer Science and Engineering, National Institute of Technology Karnataka,
Surathkal, Mangalore, Karnataka 575025, India
* E-mail of the corresponding author: harinijc@gmail.com
Abstract
This paper describes the “biologically inspired methodology” as a computing and problem solving
technique. Bio-inspired methods have recently gained importance in computing due to the need for flexible,
adaptable ways of solving engineering problems. Bio-inspired algorithms are based on the structure and
functioning of complex natural systems and tend to solve problems in an adaptable and distributed fashion.
An example of a bio-inspired approach to solving the problem of location search has been taken up and
discussed in this paper. The bio-inspired methodology has several merits and demerits, which are also
discussed in the paper.
Keywords: Bio-inspired approach, Merits and Demerits, Haptotaxis, Competitive and Cooperative
Interactions
1. Introduction
Computers have grown from rudimentary calculation machines to sophisticated complex machines that can
perform detailed and precise computations and store huge amounts of data. However, the capacity of
computers is still limited by the physical limits imposed by the raw material used to make computers.
(Nancy Forbes 2000)
Several computation techniques have been introduced to enhance computation beyond the physical limits
of computers to solve complex problems. One such approach is biologically inspired computing, also
known as Bio-Inspired approach.
Despite the numerous advances in computing technologies, we continue to be humbled by the way nature
operates. The variety, sophistication of nature has always amazed the human kind. A problem solving
methodology derived from the structure, behaviour and operation of a natural system is called a Bio-
Inspired approach. Several systems such as the ant-colony system, bee foraging, bird flocking etc. have
been used as the basis for developing models and algorithms to solve various issues such as peer-to-peer
network communication and optimal resource allocation. Bio-inspired algorithms have gained importance
in the field of computing for their remarkably flexible and adaptable nature.
2. What is Bio-Inspired Computing?
Computing has evolved to help us solve problems with increasing ease. Several complicated problems can
be solved using engineering approaches. However classical approaches to solve such problems lack in
flexibility and require rigorous mathematical analysis. In direct contrast to these approaches are new
methodologies inspired by the natural world that provide simple solutions to complex problems that would
be hard by traditional computing approaches.
Biologically inspired algorithms or bio-inspired algorithms are a class of algorithms that imitate specific
phenomena from nature. Bio-inspired algorithms are usually bottom-up, decentralized approaches (Ding
2009) that specify a simple set of conditions and rules and attempt to solve a complex problem by
iteratively applying these rules. Such algorithms tend to be adaptive, reactive and distributed. (Rocha 2011)
2.1 Importance of Bio-inspired Approach

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The study of biological organisms has recently gained importance in computing. Biological organisms deal
with environmental demands using ingenious solutions that differ greatly from engineering solutions that
are traditionally used to solve similar problems. Such biological solutions are commonplace and easily
available to study.
Inspiration has been drawn from biology since the time of early computing. The first digital computer by
von Newmann was based on the human brain. (Nancy Forbes 2004) However the use of algorithms directly
mimicking the behaviour of natural organisms is a recent development and these algorithms are proven to
be significantly more robust and adaptive than traditional algorithms while not compromising much on
performance.
Bio-inspired algorithms imitate a biological system in terms of their component behaviour. Biological
systems heavily depend on individual components of the system. Thus the first step in building a bio-
inspired algorithm is to build individual simplistic components that imitate the behaviour of their biological
counterparts. These components then try to reach the overall goal that is defined for them. The components
can then be tailored to meet specific problem requirements such as performance or adaptability.
3. An Example
There have been several papers in this area targeting specific real world problems. Researchers have
tailored generic bio-inspired approaches such as genetic engineering and ant-colony optimisation to
specialized computing problems such as developing self-organizing systems and dynamic resource
allocation. One such application is the use of the Haptotaxis phenomenon to perform a location search in an
unstructured p2p network. (Kulkarni, Ganguly, Canright & Deutsch 2006)
3.1 Introduction to Haptotaxis
Tissue development, inflammation, tumour metastasis and wound healing in the body takes place by a
phenomenon called Haptotaxis. Cell migration to a wounded area or to an inflamed area in the body must
maintain a defined direction and speed. This is achieved by cell adhesion proteins that are present in the cell
walls. Adhesion ligands are present in the extra-cellular matrix (ECM). The ECM is a layer that surrounds
cells in a tissue. The ECM creates a gradient of cell adhesion causing the cells to move towards higher
adhesion between ECM ligands and cell receptors. The magnitude of adhesion affects the speed of cell
movement while the gradient of adhesion affects direction of cell movement. The Haptotaxis phenomenon
is an example of making cells move closer to the destination. This concept can easily be applied to any
guided search problem where at any given point of time, the entity should be directed closer to the
destination.
3.2 Application
The paper “A new bio-inspired location search algorithm for peer to peer network based Internet
telephony” (Brownlee 2005) describes a method of applying the concept of Haptotaxis to the general
problem of guided search and specifically to p2p internet telephony systems such as Skype. This technique
is called the Hapto-search algorithm.
The authors assume a key based network, where the aim of the system is to retrieve location information of
other nodes in the system. Each node is assigned a key, which it distributes to a fixed number of nodes in
the network. We say that a node A knows the location information of a node B if it knows the key of B.
Thus, if a node A wants to find the location information of a node B, it tries to reach a node C which has a
key of node B. Once it finds node C, it extracts B’s location information from it.
Figure 1 depicts an instance of the use of the Hapto-search algorithm in a key based network. Assume that a
node X distributes its key to nodes C and D. A node G tries to find location information of node X. It first
searches the keys it contains to determine if it itself contains the location information of the node X If node
G does not contain the key of node it searches its neighbours to find the neighbour that is closest to the
destination. To do this, it goes through the keys that each of its neighbours contains. If any of its neighbours
contain the key of the destination node, the location information is downloaded from that node. Otherwise,
the Hamming distance between each of the keys they contain and the destination is calculated to find the
neighbour with the least distance from the destination. The closest neighbour becomes the current node.

Vol 2, No.2, 2012
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This process is repeated until one of the nodes C, D or X is found. The location is downloaded from the
node thus found.
3.2.1 Structure of the network
Every node distributes its key to a fixed number of nodes in the network. Thus every node in the network
contains keys from other nodes in the network. For any node to get the location information of a node, it is
enough if they traverse to any of the nodes that the node has distributed its key to.
3.2.2 The concept of Closeness
The Hamming distance between keys is used as a measure of closeness of one key to the other. A node A is
said to be closer to the destination than a node B if A has a key closer to the destination than B in terms of
Hamming Distance. If a node does not have the required key, it routes the query to a neighbour that is the
closest to the destination.
3.3 Problems with the algorithm
A major problem with the algorithm is when a local minimum is reached. This means the current node is
the closest to the destination than any of its neighbours. This problem arises because of the way the
algorithm is designed rather than a problem with the biological method used to model the situation. The
problem however is overcome by recognizing minima when they occur and continuing the solution using
the neighbour farthest from the destination as the current node. A minimum can be recognized easily when
all neighbours of the current node have been evaluated to have a higher Hamming Distance than the current
node.
4. Bio-Inspired Approach as a Problem Solving Technique
As described in section 2, bio-inspired algorithms depend heavily on component behaviour. They take a
bottom-up decentralized approach to solving any problem. They are called computationally intelligent with
respect to the field of artificial intelligence. This is because the system is not told how to achieve an overall
goal. Instead, through iterative individual component behaviour, the system produces an emergent, overall
behaviour. This emergent behaviour is then utilized for solving the problem.
Bio-inspired techniques have three common concepts to achieve the bottom-up emergent behaviour.
(Brownlee 2005)
a) Emergent Effects: Desirable characteristics emerge from exposing the bio-inspired computational
system to a particular problem. This phenomenon is due to individual component interaction and
are easily observed and identified in systems. There are usually complex relationships among
individual behavioural patterns that cause emergent effects.
b) Local Interactions: Local interactions are required for components to pass local information,
synchronization etc. These interactions are simplistic and are easy to describe and implement.
c) Intermediate Dynamics: The activities of a system that describe how and why discrete units and
local rules result in the desired emergent behaviours. These dynamics are complex and difficult to
model or describe. Although these concepts are very hard to model exactly for engineering
applications, each of these concepts can be optimized or modified to suit specific engineering
problems. (Abbott 2005)
4.1 Component design
Components in biological phenomena can typically be classified into cooperative or competitive,
depending on whether the components share information to reach the component-level goal or whether
each component works competitively to reach the component-level goal. (Smistad 2010)
Consider, for example, Particle Swarm Optimizations (PSO). At the beginning of the algorithm, we
randomize the location of individual members and the direction in which each member moves. Each
member of the swarm then does a local study (environment variables such as location, velocity and distance
from the original base camp). Usually in such algorithms, the individual aims to spread its information to
its neighbours. This ensures that each member gets a relative idea about its neighbours and has sufficient

Vol 2, No.2, 2012
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information about where its individual performance stands relative to the group. This information helps the
individual make decisions such as whether to keep moving in the same direction or not and whether to slow
down or speed up. These environment variables are periodically checked by each component throughout
the lifetime of the program.
If we compare this search technique to evolutionary algorithms we see that both are parallel search
techniques. But, while evolutionary algorithms have competitive interactions, PSO has cooperative
interactions among its individual components.
To illustrate competitive interactions, we take the example of Bird Flocking Algorithms. This category of
algorithms are inspired by birds and fish that move together so that no individual in the flock can be singled
out and eaten by predators. It is to be noted that as in particle swarm optimizations where the organism’s
tendency is to move towards achieving a common goal of the entire group. This may not apply in case of
Bird Flocking Algorithms because here each individual’s aim is to survive. This may come at the cost of
life of the other members. In some cases, the individual is presented with a choice to leave the group during
its migration if it senses that the group of which it is currently a part of is more susceptible to danger that
some other group.
Craig Reynolds (1986) studied these algorithms in detail and was the first one to simulate the Flocking
Behaviour on a computer. He suggested flocking behaviour is controlled by three simple rules:
a) Separation - avoid crowding neighbours (short range repulsion)
b) Alignment - steer towards average heading of neighbours
c) Cohesion - steer towards average position of neighbours (long range attraction)
The phenomena of cooperation and competition are driving forces for several complex biological systems
in nature.
5. Merits and Demerits of Bio-inspired Approach
5.1 Merits
As we have discussed, bio-inspired algorithms present several merits because such a system is designed to
be flexible, completely distributed and efficient. Bio-inspired systems can grow, organize, and improve
themselves with little direction from humans. These systems consist of several, usually quite simple,
individual components. The components usually follow some simple behaviour according to the local
information they have or can perceive. This enables artificially intelligent systems that use these
components and dynamics to do parallel processing. Each of the components can often operate separately.
A few other merits are presented in Table 1, comparing them to the methods adopted by conventional
algorithms using the following criteria.
a) Flexibility
b) Performance
c) Scalability
d) Flexibility in decision making
e) Improvement Scope and innovation
5.2 Demerits
Although Bio-inspired approaches to problem solving seems almost ideal because of properties such as
self-optimization, flexibility and simple set of ground rules, it has a few demerits.
1. Component Design: A major drawback in case of Bio-inspired algorithms is the conflict on
whether to compromise on competitive interactions or cooperative interactions.
2. Lack of data: Biological systems are extremely hard to study, and the lack of data on a system may
affect the design of the algorithm derived from the corresponding biological system. For example,
not many measurements have been made for bird flocking, even with high speed cameras to film

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flocks. The rules listed above have been found true for small groups but for large flocks the
validity of the above rules remains questionable, especially the rule regarding cohesion in large
flocks.
3. Lack of complete adaptability: Bio-inspired algorithms cannot be completely adapted to real world
systems because of conflicts in scalability or performance issues. For example, in the Bird-
flocking algorithm, achieving component safety from being singled out will require that we work
out the path of each individual in an explicit manner. This is alright when the algorithm is small
scale (similar to a flock of 15-20 birds). However, if we are dealing with a large number of
components, this explicit programming will take a huge amount of time.
4. Low performance: Bio-inspired algorithms typically have low performance. This is because
biological methods aim to behave well in a wide variety of situations as against aiming to reach
the goal quickly. (Neumann & Witt 2010) However, we are free to improve on performance by
compromising on the adaptability or flexibility of the algorithm if we know parameters about the
environment that the algorithm will be working in.
A few other demerits are presented in Table 2, comparing them to the methods adopted by conventional
algorithms using the following criteria.
a) Initial thrust/Starting condition for the algorithm
b) Overhead involved
c) Checking of the environment variables
6. Comparison of Bio-Inspired Algorithms with Conventional Algorithms
Bio-inspired approach certainly differs from the conventional techniques. Biological techniques usually are
results of efforts of generations for their struggle to survive harsh conditions. Bio-inspired algorithms are
built on simple rules and the assumption that the organism stick to those rules. Also an important
characteristic of Bio-inspired approach is the continuous checking of an individual’s own performance, as
compared to the group. Table 3 compares conventional algorithms to Bio-Inspired algorithms with respect
to four criteria
a) Intelligence
b) Testing and Verifiability
c) Improvement
d) Adaptability to practical situations (Kulkarni, Ganguly, Canright & Deutsch 2006)
It is these very properties that are coming to the fore in emerging computer environments such as
autonomic computing, pervasive computing, peer-to-peer systems, grid computing and the semantic web.
These environments demand systems that are robust to failures, adaptable to changing requirements and
deployment scenarios, composed of relatively simple components for ease of development and
maintenance and are preferably decentralized and parallel.
7. Conclusion and Future Work
Bio-inspired approach is an emerging field in problem solving techniques. Bio-inspired algorithms have the
unique feature of being highly decentralized, bottom-up, adaptable and flexible, thus providing elegant
solutions to engineering problems that are constrained by rigid limitations that traditional approaches pose.
These algorithms are being progressively used and adapted to various real life situations and problems.
In this paper, we have explored the Bio-Inspired approach and analysed its importance by way of an
illustration. Bio-Inspired Algorithms can be used as a problem solving technique using the concepts of
Emergent effects, Local Interactions and Intermediate Dynamics. The merits and demerits of using such an
approach in real life problems are also shown. This paper addresses the difference between conventional
approaches to problem solving and Bio-Inspired approaches.

Vol 2, No.2, 2012
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Bio-Inspired approach to problem solving has immense potential for research especially in the field of
Secure routing in Mobile Ad Hoc Networks. The specific challenges of secure routing in MANETs can be
handled by using Bio-Inspired Algorithms. As future work, we intend to explore the possibility and
feasibility of using an immune system inspired Bio-Inspired approach for secure routing in MANETs.
References
Abbott, R. (2005), “Challenges for Bio-inspired Computing”, The Proceedings of The BioGEC workshop,
GECCO, New York: ACM, pp. 12-22
Bongard, J. (2009), “Biologically Inspired Computing”, IEEE Computer 42(4), pp. 95-98 [online at
http://www.cs.uvm.edu/ jbongard/papers/2009 IEEEComp Bongard.pdf [accessed 11 Sep 2011]
Brownlee, J. (2005), “On Biologically Inspired Computation a.k.a. The Field”, Technical Report 5-02,
Swinburne University of Technology
Cohoon, J. & Evans, D. (2003), “Biologically-Inspired Computing”, http://www.cs.virginia.edu/evans/bio/
[accessed: 11 Sep 2011]
Craig Reynolds (1986), “Boids”, http://www.red3d.com/cwr/boids/ [accessed 11 Sep 2011]
Ding, J (2009), Advances in Network Management, Taylor & Francis, p. 164
Forbes, N. (2000), “Biologically Inspired Computing”, Computing in Science and Engineering 2(6), pp. 83-
87
Forbes, N. (2004), Imitation of Life: How Biology Is Inspiring Computing, Cambridge, Massachusetts: The
MIT Press
Kulkarni, S., Ganguly, N., Canright, G. & Deutsch, A. (2006), “A new bio-inspired location search
algorithm for peer to peer network based Internet telephony”, Proceedings of the 1st international
conference on Bio-inspired models of network information and computing systems, New York: ACM,
Article 33.
Neumann, F. & Witt, C. (2010), Bio inspired Computation in Combinatorial Optimization Algorithms and
Their Computational Complexity, (Natural Computing Series) Springer [online at
http://bioinspiredcomputation.com/self-archived-bookNeumannWitt.pdf]
Ridge, E., Kudenko, D., Kazakov, D., Curry, E. (2005), “Moving Nature-Inspired Algorithms to Parallel,
Asynchronous and Decentralised Environments”, In Czap, H., Unland, R., Branki, C., Tianfield, H. (eds),
Proceeding of the 2005 conference on Self Organization and Autonomic Informatics, IOS Press, pp. 35-49.
Rocha, L. (2011) “I485/H485/I585: Biologically Inspired Computing”,
http://informatics.indiana.edu/rocha/i-bic/. August 31, 2011 [accessed 11 Sep 2011]
Smistad, E. (2010) “Competitive and cooperative interactions in biological inspired AI”,
http://www.thebigblob.com/competitive-and-cooperative-interactions-in-biological-inspired-ai [accessed 11
Sep 2011]
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[accessed: 11 Sep 2011]
Notes
Note 1. The basic requirement of all bio inspired algorithms is that they always need some kind of initial
thrust or some kind of competition or some condition from which something has to be gained. If there is
nothing to be gained or there is no competition then these types of algorithms should not be preferred.
Note 2. Movement of every member (node) is not simply randomized but is guided by some specific set of
rules which are programmed into the system.

Vol 2, No.2, 2012
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Table 1. Merits of Bio-Inspired algorithms compared to conventional algorithms
Criteria Bio-inspired Algorithms Conventional Algorithms
Flexibility Strength through flexibility, or
strength in numbers
Start with a fixed size or
population in mind and hence are
not very flexible
Performance Work well even when the task is
poorly defined
Reach a saturation limit in their
performance
Scalability Scalability is not really a
challenge
Scalable, but only to a certain
degree
Flexibility in decision making Tend to find the alternate best
available solution
Depends on programmer’s
understanding of the program
Improvement Scope and
innovation
Largely unexplored field Conventional algorithms are
optimized and developed almost
to their limits
Table 1. Challenges while dealing with Bio-Inspired algorithms
Initial thrust/Starting condition for
the algorithm
Require some kind of initial
thrust
No other initial thrust required for
the program to run than its
specified input
Overhead involved Overhead involved in assigning a
fitness value
Overhead involved is
comparatively less
Checking of the environment
variables
They form feedback mechanisms.
Thus they need to continuously
check environment variables.
Do not require any environment
variables other than specified
input and result.
Table 2. Comparison of Bio-Inspired Algorithms to Conventional Algorithms
Intelligence They are built on simple rules.
They take a bottom-up approach.
Strictly top down approach.
Testing and Verifiability Improvements have to be tested on
generations success rate of the
organisms has to be compared with
the previously existing success
rates
Any modifications to the
algorithm can be tested and
results can be verified almost
immediately
Improvement Improving of the Bio-inspired
algorithms is not that easy because
verifiability
Can be improved whenever the
programmer finds a better
approach to problem
Adaptability to practical situations
(Brownlee 2005)
Cannot be applied to practical
problems directly, but have to be
customized to the problem
Built keeping the practical
situations and the end result in
mind

Vol 2, No.2, 2012
21
Figure 1. Sketch of a sample location information search.

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