Bio-inspired Evolutionary Computing with Context-Awareness and Collective-Effect - Technologies and Applications of Artificial Intelligence - page 99

Bio-inspired Evolutionary Computing
with Context-Awareness and Collective-Effect

Yi-Ting Chen 1 , Jeng-Shyang Pan 1 , Shu-Chuan Chu 2 , and Mong-Fong Horng 1,*

1 Department of Electronics Engineering,
National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
2 Department of Computer Sciences
Flinders University, Australia
[e-mail]

Abstract. In this study, an innovative conception is conceived to break the devel-
opment bottleneck of the traditional ECs at present. This innovative conception is
bio-inspired evolutionary computing with context-awareness and collective-effect
called as Next-Generation ECs (EC 2.0). For the property of context-awareness in
EC 2.0, the individuals are able to observe environmental information by physic
property. And, the individual can regularly and closely move to objective. In addi-
tion, the individual behaviors in collective-effect include competition, cooperation
and conflict. The conflict behaviors of individuals such as difference, contradiction
or inconsistence are considered to design the search strategy. The proposed guida-
ble bat algorithm (GBA) is the paradigm of EC 2.0. The bats governed by GBA are
able to rapidly and precisely discover the global optimal solution. The simulation
results show that the solving efficiency and solution quality of GBA are better than
BA's, even well-known HBA's.

Keywords: Next-Generation Evolutionary Computing (EC 2.0), Bio-Inspired
Evolutionary Computing, Context-Awareness, Collective-Effect, Guidable Bat
Algorithm (GBA).

1 Introduction

Optimization problem in real-world is more complex and has attracted a lot of re-
searchers to search for efficient problem-solving methods. Evolutionary computing is
the better solution to solve the optimization problems and widely applied in various
applications such as big data analysis [1, 2]. Swarm-based evolutionary computation
consists of individuals to become an adaptive system [3-5]. The data structure of solu-
tion represents an individual for the optimization problem in CE. Then, the fitness
function is used to evaluate the quality of individuals. Each individual searches the
best solution by an evolution procedure. In the traditional ECs, the individuals are
advanced by the cooperation and competition such as communication with each other

* Corresponding author.