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`@@ -113,13 +113,15 @@ DA.solve(plot=True)`
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`114`	`114`	`*RESULTS*`
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`116`		`-From the console we can deduce the optimal result (see Fig. 4) . The global optimal has a fitness of 25, while duelist algorithm found 25.01. So Duelist Algorithm performs well.`
	`116`	`+From the console we can deduce the optimal result (see Fig. 4) . The global optimal has a fitness of 25, while duelist algorithm found 25.01. Error of 0.01 can be reduced by putting more maximum generations in solver.`
	`117`	`+`
`117`	`118`	`![result1](images/results.PNG)`
`118`	`119`	`Fig 4. Optimal Results in Console`
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`120`	`121`	`We can see that the algorithm quickly converges to the optimal point (see Fig. 5) as reported by the original article.`
`121`		`-![result2](images/results2.png)`
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	`123`	`+![result2](images/results2.png)`
	`124`	`+Fig 5. Quick convergence of Duelist Algorithm`
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`124`	`126`	`For more examples refer to the python scripts in "examples" folder. [https://github.com/tsyet12/Duelist-Algorithm-Python/tree/master/examples]`
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