
Programme
Overview
Detailed
Programme
Evolutionary
Computation II (ECII) 
CHAIR: MARIO KOEPPEN
Monday, March 21st,
14h2016h00
ECII1 

Title: 


Implementation and Experimental
Validation of the Population Learning Algorithm Applied to
Solving QAP Instances 
Author(s): 
J. Jedrzejowicz,
P. Jedrzejowicz 
Abstract: 
The paper proposes an implementation of the
population learning algorithm designed to solve instances
of the quadratic assignment problem. A short overview of the
populationlearning algorithm and a more detailed presentation
of the proposed implementation is followed by the results
of computational experiments carried. Particular attention
is given to investigating performance characteristics and
convergence of the PLA. Experiments have focused on identification
of the probability distribution of solution time to a suboptimal
target value. 

ECII2 

Title: 


Estimating the distribution in
an EDA 
Author(s): 
S. Shakya,
J. McCall,
D. F. Brown 
Abstract: 
This paper presents an extension to our work
on estimating the probability distribution by using a Markov
Random Field (MRF) model in an Estimation of Distribution
Algorithm (EDA). We propose a method that directly samples
a MRF model to generate new population. We also present a
new EDA, called the Distribution Estimation Using MRF with
direct sampling (DEUMd), that uses this method, and iteratively
refines the probability distribution to generate better solutions.
Our experiments show that the direct sampling of a MRF model
as estimation of distribution provides a significant advantage
over other techniques on problems where a univariate EDA is
typically used. 

ECII3 

Title: 


Modelling Chlorine Decay in Water
Networks with Genetic Programming 
Author(s): 
Philip Jonkergouw,
Ed Keedwell,
SoonThiam Khu 
Abstract: 
The disinfection of water supplies for domestic
consumption is often achieved with the use of chlorine. Aqueous
chlorine reacts with many harmful microorganisms and other
aqueous constituents when added to the water supply, which
causes the chlorine concentration to decay over time. Up to
a certain extent, this decay can be modelled using various
decay models that have been developed over the last 50+ years.
Assuming an accurate prediction of the chlorine concentration
over time, a measured deviation from the values provided by
such a decay model could be used as an indicator of harmful
(intentional) contamination. However, current chlorine decay
models have been based on assumptions that do not allow the
modelling of another species, i.e. the species with which
chlorine is reacting, thereby limiting their use for modelling
the effect of a contaminant on chlorine. This paper investigates
the use of genetic programming as a method for developing
a mixed secondorder chlorine decay model. 

ECII4 

Title: 


Evolving Blackjack Strategies Using
Cultural Learning 
Author(s): 
Dara Curran,
Colm O’Riordan 
Abstract: 
This paper presents a new approach to the evolution
of blackjack strategies, that of cultural learning. Populations
of neural network agents are evolved using a genetic algorithm
and at each generation the best performing agents are selected
as teachers. Cultural learning is implemented through a hidden
layer in each teacher’s neural network that is used
to produce utterances which are imitated by its pupils during
many games of blackjack. Results show that the cultural learning
approach outperforms previous work and equals the best known
noncard counting human approaches. 

ECII5 

Title: 


TwoCriterion Optimization in State
Assignment for Synchronous Finite State Machines using NSGAII 
Author(s): 
Nitin Gupta,
Vivek Kumar Agrawal 
Abstract: 
One of the challenging problems in circuit implementations
is finding the best state assignment for implementing a synchronous
sequential circuit which are also represented as Finite State
Machines. This problem, commonly known as State Assignment
Problem (S.A.P.), has been studied extensively because of
its importance in reducing the cost of implementation of circuits.
The previous work on this problem assumes the number of coding
bits as constant, making it a single objective problem with
the only objective being to reduce the cumulative cost of
transition between the connected states. In this work, we
add another dimension to this optimization problem by introducing
a second objective of minimizing the number of bits used for
assignment. This is desirable to reduce the complexity and
the cost of a circuit. The two objectives are conflicting
and thus the optimal solution requires a tradeoff. We present
an evoluationaryalgorithm based approach to solve this multidimensional
optimization problem. We compare the results from two algorithms,
and find that an NSGAII based approach, with some modifications
to constraint handling, gives better results and running time
than NSGA. We also gain some insights about the shape of the
efficient frontier. 

