Nischal S
Introduction
 Evolutionary Computation is the field of study devoted to the
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design, development, and analysis is problem solvers based on
natural selection (simulated evolution).
Evolutionary programming was introduced by Lawrence J. Fogel
in the US, while John Henry Holland called his method a genetic
algorithm.
Evolution has proven to be a powerful search process.
A computational model of evolution and suggested that
Darwinian evolution be studied in the framework of
computational learning theory.
An evolution as a restricted form of learning where exploration is
limited to a set of possible mutations and feedback is received
through the survival of the organisms that adapt to environment
using mutation.
Introduction(cntd..)
 Darwinian evolution is one of the most important scientific theories and
suggests that complex life-forms emerged from simpler ones. Yet, the nature of
the complexity that can evolve in organisms and the processes therein are not
well understood. The two central aspects of Darwin’s theory are
1) creation of variation due to mutations , and
2)natural selection
among the variants, a.k.a. survival of the organism that adapt best to
environment surrounding them.
 Underlying DNA sequence or genome of an organism contains code for
proteins and also encodes rules governing their regulation.
 The genome almost entirely controls the functions of an individual organism.
Eg: a function encoded in the genome of an organism could be a circuit that
decides the level of enzyme activity based on the environmental conditions
(e.g.temperature, presence of oxygen etc.)
 Captures the central ideas of mutation(random variation) and natural
selection. Understanding evolution in the framework of computational
learning theory, and understand the evolutionary process as a restricted form
of learning.
Cellular level
 The goal of computational learning theory is to separate concept classes that can be
efficiently learned (ideal) from those that cannot.
 Quantify the notion of complexity by mathematical functions, they realize.
 Mathematical function - Protein expression level: y=f(x1,x2,x3,x4..xn)
 Where x1,x,2,x3 are concentration level of each molecules. What are those functions, how
complex are they? How do they succeed in finding optimal expression level given
environment factors?
 Notion of Ideal Behavior(fi): Optimal Expression level for every possible conditions.
 Performance: How close actual fn is to Ideal functions fi? Again this depends on
environment conditions distributed over x1,x2,x3..xn. Expresses the fitness function.
 Functions to evolvable? (constrains)
 Reasonable size of population
 Reasonable number of generations.
 Not consideration of population dynamism(allele
frequency)
 We find to quantify the process of evolution through
ideal function, performance, representation,
mutation, selection, goal, evolvability and few other
functions.
Questions:
 The compare few models in computation learning theory :
 probably approximate correct (PAC) learning framework[1]
 Kearns’ statistical query learning (SQ) framework
 Correlational statistical query (CSQ) learning framework[2]
 Valiant’s model of evolution[3].
 The variants introduced by Feldman [4], Fisher [5], Muller [6] and
P. Valiant [7] are compared.
 We study and understand the nature of complexity that can
emerge in these genetic circuits, by understanding existing
models from computational learning theory. The connection of
evolvability to statistical query learning. We define the merits,
accelerations and limitations of each of these models.
Applications
 Machine Learning
 Routing in Communications Networks
 Robotics
 Pattern Recognition
 VLSI Circuit Layout
 Market Forecasting
 Path Planning
Bibliography
 [1] Leslie G. Valiant. A theory of the learnable. Communications of the
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ACM, 1984.
[2] Michael J. Kearns. Sq learning. Journal of Computing, 1998.
[3] Leslie G. Valiant. Evolvability. Journal of the ACM, 2009. Earlier
version appears as Leslie G. Valiant. Evolvability. ECCC Technical
Report TR06-120.
[4]. Vitaly Feldman. Robustness of evolvability. In Proceedings of the
Conference on Learning Theory (COLT), 2009.
[5]. R. A. Fisher. The genetical theory of natural selection. Clarendon
Press, 1930.
[6]. H. J. Muller. Some genetic aspects. The American Naturalist,
66(703):118– 138, 1932.
[7]. Paul Valiant. Evolvability of real-valued functions. In Proceedings
of Innovations in Theoretical Computer Science (ITCS), 2012.