**2.4 Probabilistic ideas**

*Epilepsy - Advances in Diagnosis and Therapy*

**2.3 Evolutionary computation**

solution for the problem.

that deals with a problem by the level of truth values which lie between 0 and 1. Fuzzy refers to vagueness. The Boolean logic results in true or false for the question (**Figure 1**) "Is it raining?" but fuzzy logic gives a number in the range from 0 to 1.

This is a logic used for fuzziness. It was introduced in 1965 by Lofti A.Zadeh. Fuzzy classifier is a classifier (algorithm) that uses fuzzy logic for classification and prediction problems. It is based on fuzzy sets (membership functions). The data-driven and trial and error (heuristic) approaches are two different approaches of fuzzy logic. An automated system can be designed using these approaches. Among these approaches, data-driven is most essential for the model to learn and update continuously. Fuzzy logic uses trial and error approach in tuning process for obtaining a satisfactory result. It is a technique that can handle imprecise data and especially analyze crisp/standard data. The data-driven approach is similar to event-driven approach and it is well structured. In classification processes, appropriate features are required to train and test the system. The performance of the system depends on selecting the apt features from the data for modeling the detection system. The heuristic method is not an optimal approach for problem-solving. It gives satisfactory solution. Heuristics, hyper-heuristics, and meta-heuristics are commonly used with machine learning and optimization techniques. Mostly, machine learning techniques are heuristic. Genetic algorithm or any optimization technique can be used to get optimal solution for the given problem. Fuzzy if then rule is the simple form of fuzzy rule based classifier. Fuzzy if-then rule statements are the form of fuzzy logic. Any classifier that uses fuzzy logic is fuzzy rule based classifier. These classifiers are well suited for linear model of classification whereas ANN can predict better on test data. Recently, deep learning has been the popular tool for prediction and detection processes. Fuzzy logic gives multi-value answers, whereas in machine learning, the system learns from data especially with the control or supervisor [2].

Evolutionary computation (EC) is a subdiscipline of AI and soft computing. In computational intelligence, evolutionary algorithms are inspired by biological systems and give optimal solution for problems. Meta-heuristic and swarm intelligence may also yield enough good solutions for any optimization problem. EC is a computational intelligence method involved in a lot of optimization techniques for problem-solving methods. It is a subfield of AI. The algorithms of EC are inspired by biological evolution. These algorithms can give highly optimum solutions for any kind of problems. Ant colony optimization, genetic algorithm (GA), genetic programming, self-organization maps, competitive learning, and swarm intelligence are some examples of EC techniques. Genetic algorithm is a technique used for optimization in problem-solving of various fields. It is derived from the natural genetic systems. It gives accurate results, exhibits robustness, and produces optimal

Here 1.0 represents absolute truth and 0.0 represents absolute false.

**198**

**Figure 1.**

*Example for fuzzy logic.*

Both probabilistic ideas and logic are used in probabilistic reasoning in order to handle uncertainty situations. Most of the problems use probability and statistics. "Clean data is greater than more data." Machine learns from data. Quality of data is important rather than quantity of data. Bayesian analysis is one of the most important approaches for probabilistic reasoning. Unknown information or imperfectness is the situation of uncertainty. Bayesian inference is a statistical inference based on Bayes theorem that can be used for accurate prediction. It is very useful when the available data are insufficient for solving the problem. Data analysis is a procedure of evaluating data that are gathered from various sources. The soft computing techniques play a challenging part in data analysis. For example, data mining techniques are especially used for discovering new information from a huge database, whereas soft computing techniques mimic the process of human brain in order to find effective solutions for any NP-complete problem.
