Genetic Algorithm and Firefly Algorithm in a Hybrid Approach for Breast Cancer Diagnosis
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2016 by IJCTT Journal | ||
| Volume-32 Number-1 |
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| Year of Publication : 2016 | ||
| Authors : Fatma Mazen, Rania Ahmed AbulSeoud, Amr M. Gody | ||
| DOI : 10.14445/22312803/IJCTT-V32P111 |
Fatma Mazen, Rania Ahmed AbulSeoud, Amr M. Gody "Genetic Algorithm and Firefly Algorithm in a Hybrid Approach for Breast Cancer Diagnosis". International Journal of Computer Trends and Technology (IJCTT) V32(2):62-68, February 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Feed-forward neural networks are
popular classification tools which are broadly used
for early detection and diagnosis of breast cancer.
In recent years, a great attention has been paid to
bio-inspired optimization techniques due to its
robustness, simplicity and efficiency in solving
complex optimization problems. In this paper, it is
intended to introduce a Genetic Algorithm based
Firefly Algorithm for training neural networks. The
proposed algorithm is used to optimize the weights
between layers and biases of the neuron network in
order to minimize the fitness function which is
defined as the mean squared error. The simulation
results indicate that better performance of the
Firefly Algorithm in optimizing weights and biases is
obtained when being hybridized with Genetic
Algorithm. The proposed algorithm has been tested
on Wisconsin Breast Cancer Dataset in order to
evaluate its performance and the efficiency and
effectiveness of the proposed algorithm by
comparing its results with the existing methods. The
results of the proposed algorithm were compared
with that of the other techniques Firefly Algorithm,
Biogeography Based Optimization, Particle Swarm
Optimization and Ant Colony Optimization. It was
found that the proposed Genetic Algorithm based
Firefly Algorithm approach was capable of
achieving the lowest mean squared error of 0.0014
compared to other algorithms as mean squared
error values for other algorithms were 0.002 for
Firefly Algorithm, 0.003 for Biogeography Based
Optimization, 0.0135 for Ant Colony Optimization ,
0.035 for Particle Swarm Optimization.
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Keywords
MLP, classification, meta-heuristic
optimization, breast cancer, Firefly Algorithm (FA),
Genetic Algorithm (GA).