1. Advanced Artificial Intelligence Evolutionary Search Algorithm
Chung-Ang University, Wangduk Seo
The original version of this content is available from the Wikipedia
Hello, my name is Wangduk Seo and my presentation title is an Evolutionary Search Algorithm

2. Introduction
Through a history of computer science and engineering, many algorithms are developed and proposed to solve various difficult problems.
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3. However…
However, this cartoon describes the difficulty of real world problem to adopt an algorithm to problem. In similar cases, there are gap between a theoretic approach and real world problem.
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4. However… Global Optima
Assuming there is a global optima which we want to find, one major reason may be hard to find global optima in proper time.
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5. as good as GLOBAL OPTIMA
New Approach
Find good solution
as good as GLOBAL OPTIMA
Applicable to various problems
So new approach was proposed. First, rather find a global optima, try to find approximate answer enough to compromise. Second, develop a general method that can be applicable to various problems.
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6. Heuristic and Metaheuristic
𝑺𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝟏
𝑺𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝟐
𝑺𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝟑
...
Available Information
Better
This new approach is called heuristic and metaheuristic. In greek, heuristic means “find” or “discover”. A heuristic is any approach to problem solving that employs method, not guaranteed to be optimal, but find approximate answers and improve them using available information in problem.
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7. Metaheuristic Metaheuristic ( = Higher level of heuristic) Heuristic
Applicable
Metaheuristic is a higher-level heuristic designed to find, generate, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.
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8. Evolutionary Search Algorithm
Evolutionary Algorithm
= Nature Inspired Metaheuristics
Evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection.
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9. Various Evolutionary Algorithms
Genetic Algorithm
Particle Swarm Optimization
Ant Colony Optimization
Cuckoo Search Algorithm
These are popular evolutionary algorithms. Genetic Algorithm is inspired by the process of natural selection. Particle swarm optimization is inspired the movement of organisms in a bird flock. Ant colony optimization is a probabilistic technique to find good path through graphs imitating a behavior of ant. Cuckoo search is inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in other birds’ nests.
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10. General Steps
General Steps are described in the picture. First, initialize the population. Then, evaluate each individual in the population. Finally, improve them by various operators such as selection, crossover, and mutation.
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11. 2 Keypoints of Evolutionary Algorithm
“How can we find good approximate solution?”
Exploration
Exploitation
There are 2 keypoints in evolutionary algorithms to find good approximated solution, which are exploration and exploitation.
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12. Exploration We should search solution space as large as possible!
Since search space is usually large, exploration is important that search large space can increase the chance to find good solutions.
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13. Exploitation We should search around good solutions!
Also, since good solutions are likely to be around other good solutions, exploitation is that search around good solutions.
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14. Exploration vs Exploitation – Trade off
However, these two concepts are conflicted to each other in terms of search space, and balancing them is one of the important problem of designing an evolutionary algorithms.
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15. Exploration vs Exploitation – Trade off
These graph shows that various algorithm balance 2 concepts variously because of different algorithm design purpose.
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16. Genetic Algorithm
A genetic algorithm is the most popular algorithm in the evolutionary algorithms and one of the most studied algorithm. A genetic algorithm is famous for its wide variety of problems.
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17. Genetic Algorithm
Genetic algorithm’s process is Initialization, Evaluation, Selection, Genetic Operators. Genetic operators are three steps that crossover, mutation and reproduction.
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18. Initialization and Evaluation
Initialization make individuals randomly or guided to start with good individuals. In evaluation step, all individuals are scored and ranked by its own fitness function such as path cost, accuracy.
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19. Selection
In selection steps, algorithm select individuals to reproduce new individuals by genetic operators. This picture shows the Random Wheel Selection that individuals are selected biased by its evaluated score.
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20. Crossover
Single-point Crossover
Uniform Crossover
Two-point Crossover
Genetic operators make new individuals from selected individuals. Crossover produce new individuals such as swapping selected individuals. For example, single-point crossover choose one crossover point randomly and swap individuals based on the crossover point.
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21. Mutation
Mutation is a genetic operator used to maintain genetic diversity from one generation of a population to the next. This mutation is swap mutation that swap entities randomly in a individual.
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22. Example “THISISANEGG” “THIS IS AN EGG”
Let’s see how genetic algorithm works. There is one sentence without any space. Our goal is insert space properly.
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23. Representation “THISISANEGG” “THIS IS AN EGG” Evaluation Score : 6 1 11
Search Space : 𝟐 𝟏𝟎
Evaluation
Sample 1
Score : 6
Ground Truth Answer (Hard to know in real world) 1
These sentences can be represent by binary string that 0 means now space, and 1 means space. Choosing 0 or 1 in each entities, the search space of this problem is 2 power 10. To evaluate individuals, the ground truth answer are compared with the individuals counting the correct entities. Usually, the ground truth answer are blinded. Since this example’s purpose is to know how GA works, we assume that we know the ground truth answer for special case.
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24. Initialization and Evaluation
Randomly Initialized (Population size = 4)
Crossover
Score 1 1 6
Evaluation 1 1 7 1 1 4
Mutation 1 1 5
4 individuals are randomly initialized. Each individuals are evaluated by comparing the answer. Let’s assume two best individuals are selected for crossover and one individuals are selected randomly for mutation.
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25. Crossover Score 1 Crossover 1 5 1 1 8 Crossover point1
Crossover 1 5 1 1 8
Crossover point
By single point crossover, two new individuals are generated. Crossover point are randomly selected and swap them. The generated individuals’ scores are 5 and 8.
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26. Mutation Score Mutation 1 1 61 6
Swap mutation is operated to generate new individuals. Two points are randomly selected and swapped. The new generated individual’s score is 6.
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27. Make New Generation Score New Generation Score 1 1 1 1 1 6 1 1 1 1 7 11 6
New Generation
Score 1 7 1 6 1 4 1 7 1 5 1 8 1 5
Crossover 1 6 1 8 1 6
Mutation
Through the genetic operators, 3 individuals are generated and choose individuals order by its score as many as the size of population. Repeat until the stop criterion is satisfied.
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28. Design Issue in GA 1. Problem Modeling 2. Evaluation Method
3. Algorithm Selection in each stages
There 3 algorithm design issues in Genetic Algorithm. First, Problem modeling such as data representation, hyper-parameter setting. Second, design evaluation method to identify good solutions and bad solutions. Finally, since there are several algorithms in each stages of Genetic Algorithm, appropriate algorithms must be selected for good performance.
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29. In real world…
In real world, Evolutionary algorithm is applied various field. Left picture is NASA ST5 spacecraft antenna. This complicated shape was found by evolutionary computer design program to create the best radiation pattern. Right picture is find walking mechanism by Genetic Algorithm through generations.
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30. Q & A
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