Here are the answers to the brain teasers: 1. Levy flights are modified in Cuckoo Search algorithm. 2. John Holland was the first person to work on evolutionary computation in the 1970s. 3. Evolutionary algorithms are preferred because they can search very large spaces of possible solutions and do not require derivatives or other auxiliary knowledge to find the optima. 4. Some shortcomings of PSO overcome by Cuckoo Search include getting stuck in local optima and its inability to utilize the Lévy flights for better exploration of the search space.

1. First National Conference on
Algorithms and
Intelligent Systems under Lead
College Activity Shivaji University.
In association with
Computer Society on India and International Journal of
Computer and Communication
Technology.

2. Paper Title
Immune Artificial Cuckoo Search algorithm for
Nonlinear System Identification.
Paper ID – 45
Ritesh kumar Lav Kedia
School of electrical sciences Electrical Engineering
IIT Bhubaneswar BIT Deoghar

3. CONTENTS…
 Keywords
 FLANN
 Problem Statement
 Cuckoo Search Algorithm
 Introduction to Immunity and its Need
 Advantages
 Pseudocode
 Conclusion

4. .
Immune Artificial Cuckoo Search
algorithm for Nonlinear System
Identification

5. KEYWORDS
 FLANN
 NON-LINEAR SYSTEM
 CUCKOO SEARCH ALGORITHM
 IMMUNE SYSTEM

6. FLANN (FUNCTIONALLY LINKED
ARTIFICIAL NEURAL NETWORK)
 FLANN was first proposed by Pao as a single layer
ANN structure capable of forming arbitrarily wide
complex decision regions by generating non-linear
decision boundaries.
 FLANN has only input and output layers and the
hidden layers are completely replaced by the
nonlinear mappings.
 FLANN increases the learning rate but also
reduces the computational complexity.

7. CONTD…

8. Non-Linear plant Trigonometric FLANN

9. PROBLEM
To frame algorithm that would be able to model the
non-linear systems what we encounter in our day-2-
day life.

11. NEST
CUCKOO BREEDING
eggs
Cuckoo

12. OR
CROW

13. LEVY FLIGHTS
 Random walk in which the step-lengths are
distributed according to a heavy-tailed probability
distribution. After a large number of steps, the
distance from the origin of the random walk tends to
a stable distribution.

14. IMMUNE ACSO: A BETTER
APPROACH TOWARDS
OPTIMISATION
 Introducing the concept of immunity in AFSO is a
novel way of development of new algorithms using
natural swarm intelligence along with the self-
sustaining capability of immune systems.
 There are several ways of making any optimisation
algorithm immune. These include Negative
selection algorithm, clonal selection algorithm and
Immune Network algorithm.
 For our purpose, we have used clonal selection
algorithm.

15. CLONAL SELECTION
 Widely accepted model for how the IMMUNE
SYSTEM responds to an invasion from outside.
 Some of its basic steps are as follows:
1. For an attack from outside there is always a
particular type of receptor cell for detecting it.
2. After an attack specific type of receptor cells are
activated and are differentiated to form more
number of identically specific cells.
3. So for an attack of similar type these receptor
cells will come into function quite early and hence
reduces the risk of cells damage.

16. CLONAL SELECTION
IN PICTURE

17. ADVANTAGES OF IMMUNE
AFCSO
 Immune AFSO has a better convergence rate as
compared to its non-immune counterpart.
 The probability of getting stuck in a local solution
also gets minimised.

18. PSEUDOCODE
Begin
Objective function f(x)=(X1,X2,...........)
Generate initial population of n host nests Xi
While (t<max.generation) or (stop criterion)
Get a cuckoo randomly by Levy Flights.
Evaluate its Fitness,Fi.
Choose a nest randomly say j .
If (Fi>Fj)
Replace J by the new solution
End if
A fraction (pA) of the worst nests are abandoned and new ones are
built.
Keep the best solution.
Rank the solutions and find the current best
End while
Use genetic algorithm based crossover and mutation technique to
find the solution to the problems.
Post process results and visualisation.
End.

19. REFERENCES
 X.-S. Yang; S. Deb (December 2009). "Cuckoo search via Lévy
flights". World Congress on Nature & Biologically Inspired Computing
(NaBIC 2009). IEEE Publications. pp. 210–214.
 R. N. Mantegna, Fast, accurate algorithm for numerical simulation of
Levy stable stochastic processes, Physical Review E, Vol.49, 4677-
4683 (1994).
 J.J.,1989a.AdaptiveIIRfiltering.IEEEASSPMagazine,421.
1989,chapter 8,pp.197-222.
 X.-S. Yang and S. Deb, "Engineering optimisation by cuckoo search",
Int. J. Mathematical Modelling and Numerical Optimisation", Vol. 1,
No. 4, 330-343 (2010).
 N. Bacanin, An object-oriented software implementation of a novel
cuckoo search algorithm, Proc. of the 5th European Conference on
European Computing Conference (ECC'11), pp. 245-250 (2011).

20. CONTD…
 M. Tuba, M. Subotic, and N. Stanarevic, Modified cuckoo search
algorithm for unconstrained optimization problems, Proc. of the 5th
European Conference on European Computing Conference
(ECC'11), pp. 263-268 (2011).
 S. Walton, O. Hassan, K. Morgan, Using proper orthogonal
decomposition to reduce the order ot optimization problems, in: Proc.
16th Int. Conf. on Finite Elments in Flow Problems (Eds. Wall W.A.
and Gvravemeier V.), Munich, p.90 (2011).
 F. Wang, L. Lou, X. He, Y. Wang, Hybrid optimization algorithm of
PSO and Cuckoo Search, in: Proc. of 2nd Int. Conference on
Artificial Intelligence, Management Science and Electronic
Commerce (AIMSEC'11), pp. 1172-1175 (2011).
 A. Kumar and S. Chakarverty, Design optimization for reliable
embedded system using Cuckoo Search,in: Proc. of 3rd Int.
Conference on Electronics Computer Technology (ICECT2011), pp.
564-268 (2011).

21. BRAIN TEASERS…
 So which famous algorithm is modified in Cuckoo
Search algorithm??
 So can anyone tell me that who was the first person to
work on evolutionary computation??
 Why do we prefer evolutionary algorithms over others to
solve or optimize our problems??
 So what all shortcomings of PSO are overcome by
Cuckoo search algo.??